11. Taxing (Shadow) Banks: A Pigovian Model

The original but condensed version of this chapter appeared in L. Nijs, (2015), Neoliberalism 2.0. Regulating and Financing Globalizing Markets, Palgrave, Basingstoke, pp. 251–324. This chapter constitutes an extended and updated version.

There are those that argue shadow banking provides a channel for banks with efficient investment opportunities to escape regulation such that, even though involving higher risks, it improves welfare. Ordoñez proposes a novel intervention that taxes shadow activities and subsidizes regulated activities to implement an even superior allocation and allow banks to self-select into being regulated or not. See: G. Ordoñez, (2018), Sustainable Shadow Banking, American Economic Journal: Macroeconomics, Vol. 10, Nr. 1, January, pp. 33–56; A. Moreira and A. Savov, (2017), The Macroeconomics of Shadow Banking, The Journal of Finance, Vol. 72, Issue 6, pp. 2381–2432.

It impacts the welfare of that agent.

I. Kumekawa, (2017), The First Serious Optimist: A.C. Pigou and the Birth of Welfare Economics, Princeton University Press, Princeton NJ. Pigou is also seen as the birthfather of welfare economics as he believed in the consensus of the great and the good; see the book review by H. James, (2017), The Crusty Professor, IMF Journal ‘Finance & Development, June, p. 59. In contrast to that he also seems to be known in a late-Victorian tradition for misogyny and his belief of the inferiority of women; see for a somewhat accommodating review: N. Aslanbeigiu, (1997), Rethinking Pigou’s Misogyny, Eastern Economic Journal, Vol. 23, Issue 3, pp. 301–316.

Recently, it has been argued that the Pareto optimum cannot always be achieved through a compensation mechanism. Precise targeting of compensating transfers, however, may not be possible when agents are heterogeneous and the planner faces constraints on the design of transfers. Compensation is a prediction problem. Therefore Sallee derives a necessary condition for an efficiency-enhancing policy to create a Pareto improvement that can be tested directly with data. The condition relates the size of efficiency gains to the degree of predictability between initial burdens and variables used to determine a transfer scheme. He further demonstrates how heterogeneity and predictability more generally impact a planner’s ability to control who ultimately loses and by how much and at what cost. Results indicate that it is infeasible to create a Pareto improvement from the taxation of these goods (which refers in his case to gasoline taxes), and moreover that plausible policies are likely to leave a large fraction of households (and other agents) as net losers. It is highlighted here for reasons of completeness, but as the problem doesn’t affect the relevance of a Pigovian model in the case of financial services and systemic risk, it will not be discussed further in detail. See in detail: J.M. Sallee, (2019), Pigou Creates Losers: On the Implausibility of Achieving Pareto Improvements from Efficiency-Enhancing Policies, NBER Working paper Nr. 25831, May.

See for an extensive review of the Pigovian model: L. Nijs, (2015), Neoliberalism 2.0. Regulating and Financing Globalizing Markets, Palgrave, Basingstoke, pp. 156–219.

It is easy to understand legitimation of the tax function. Tax simplicity has many benefits; see: Ph. Aghion et al., (2017), Tax Simplicity and Heterogeneous Learning, NBER Working paper Nr. 24049, November.

See, for instance, H. Nabilou, (2017), Regulatory Arbitrage and Hedge Fund Regulation: The Need for a Transnational Response, Fordham Journal of Corporate & Financial Law, Vol. 22, Nr. 4, pp. 557–603; M. Laura and N. Fahad, (2017), Would Hedge Fund Regulation Mitigate Systemic Risk? Direct vs. Indirect Regulation Approach, International Business Research, Vol. 10, Issue 8, pp. 31–43; B. Munyan, (2015), Regulatory Arbitrage in Repo Markets, OFR Working Paper Nr. 15–22, October 29. He tested and concluded that about 170 Billion USD are temporarily removed from the US market for tri-party repurchase agreements (repos) before each quarter-end in order to appear safer and less levered. This occurred particularly in banks with relatively low capital ratios. Also: EBI, (2017), Shadow Banking and Systemic Risk, EBI Working Paper Nr. 1. See regarding the link between foreign investments and regulatory arbitrage: W.S. Frame et al., (2017), Foreign Investment, Regulatory Arbitrage, and the Risk of U.S. Banking Organizations, Federal Reserve Bank of Atlanta Working Paper Nr. 2, March. It was documented that ‘U.S. BHCs are more likely to operate subsidiaries in countries with weaker regulation and supervision and that such location decisions are associated with elevated BHC risk and higher contribution to systemic risk.’

Also pointing in that direction: J. Masur and E.A. Posner, (2015), Towards a Pigovian State, University of Chicago Public law and Legal Theory Working Paper, Nr. 503, February. Masur and Pozner indicate that the principal reason regulators do not employ Pigovian taxes is that they do not believe they have the authority to do so under existing law and that Pigovian taxes may lack political support because they do not serve the interests of those with political power (pp. 3–4). They demonstrate in a variety of industries and regulatory fields that a Pigovian model would be feasible or even preferable (pp. 11 ff). But regulators refrain from using them as taxes tend to have a negative connotation and that the divide between regulation and taxes is firmly entrenched in Western public institutional organizations (pp. 31 ff). See for their take on Pigovian taxes and the financial industry (pp. 21 ff).

See, for instance, M. Raddant and D.Y. Knett, (2016), Interconnectedness in the Global Financial Market, OFR Working Paper Nr. 16-09, September 27.

See for a write-up of the process of credit intermediation in the shadow banking system taking explicit account of traditional banks’ role as creators of means of payment. See in detail R. Unger, (2016), Traditional Banks, Shadow Banks and the US Credit Boom – Credit Origination Versus Financing, Deutsche Bundesbank Discussion Paper Nr. 11, Frankfurt.

See also: J. Ohis et al., (2016), International Banking and Cross-Border Effects of Regulation, Deutsche Bundesbank Discussion Paper Nr. 27, Frankfurt am Main. Their focus is on the responses to the regulatory framework actually implemented including prudential instruments and the quest for regulatory arbitrage and shifting of activities following ex ante directional regulation being implemented. So, the question being asked is how prudential policies implemented in domestic and foreign markets affect (German) banks’ local and global lending behavior. They conclude that spillover effects exist but with heterogeneous results depending on the direction of spillovers, the type of banks and prudential instruments, that is, more local loan supply as a response to stricter regulation in countries in which they maintain international activities. In contrast, foreign banks (located in Germany) reduce their loan supply (in Germany) if regulation in their home country tightens. This suggests that no general policy conclusions can be drawn and piecemeal analysis is required. For alternatives: M. Bussière et al., (2016), International Banking and Cross-Border Effects of Regulation: Lessons from France, Banque de France Working Paper Nr. 599, August and O. de Bandt and M. Chahad, (2016), A DSGE Model to Assess the Post-Crisis Regulation of Universal Banks, banque de France Working Paper Nr. 602, September; J. Berrospide et al., (2016), International Banking and Cross-Border Effects of Regulation: Lessons from the United States, Board of Governors of the Federal Reserve System, International Finance Discussion Papers Nr. 1180, September. Domestic prudential regulation can have unintended effects across borders as it shifts capital allocation; H.E. Damar and A. Mordel, (2016), International Banking and Cross-Border Effects of Regulation: Lessons from Canada, Bank of Canada Working Paper Nr. 34, July; J. Frost et al., (2016), International Banking and Cross-Border Effects of Regulation: Lessons from the Netherlands, DNB Working Paper Nr. 520, September; K. Ho et al., (2016), International Banking and Cross-Border Effects of Regulation: Lessons from Hong Kong, HKIMR Working Paper Nr. 12, July.

R. Engle et al., (2015), Global Systemic Risk: What’s Driving the Shadow Banking System?, Institute of Global Finance Volume 1. Nr. 1, Research Paper Nr. 1, July.

Ph. Stevens, (2014), Nothing Can Dent the Divine Right of Bankers, Financial Times, January 16.

L. Nijs, (2011), Shaping Tomorrow’s Marketplace. Investment Philosophies for Emerging Markets and a Semi-Globalized World, Euromoney, London, pp. 145–159.

See for an evolution of the regulatory capital requirements starting Basel I: M.C. Plossner and J.A.C. Santos, (2018), The Cost of bank Regulatory Capital, FRB of NY Staff Report Nr. 853, June.

Schwarcz indicates that the lack of enforceability creates uncertainty and undermining predictability. He suggests that soft law can be ‘chosen’ as the governing law between contracting parties. See in detail: S.L. Schwarcz, (2019), Soft Law as Governing Law Duke Law School Public Law & Legal Theory Series Paper Nr. 2019-8, January 29.

See for an analysis: E. Lee, (2014), The Soft law Nature of Basel III and International Financial Regulations, University of Hong Kong Working Paper, Mimeo.

H. Gribnau, (2007), Soft Law and Taxation: The Case for the Netherlands, Legisprudence, Vol. 1, Issue 3, pp. 296–297. See also H. Gribnau, (2008), Soft Law and Taxation: EU and International Aspects, Legisprudence, Vol. 2, Issue 2, pp. 67–117.

L. Senden, (2005), Soft Law, Self-Regulation and Co-Regulation in European Law: Where Do They Meet? Electronic Journal of Comparative Law, Vol. 9, p. 17. See in detail regarding soft law: U Mörth (ed.), (2004), Soft Law in Governance and Regulation: An Interdisciplinary Analysis, Edward Elgar Publishing, Cheltenham.

Economic uncertainty and risk appetite appear to play a limited role in explaining ex ante credit risk. There is much more evidence of cross-border spillover effects of monetary policy, that is, interest rate policy determines risk-taking in the credit market. See: S.J. Lee et al., (2016), Risk Taking and Interest Rates: Evidence from Decades in the Global Syndicated Loan Market, IMF Working Paper Nr. 17/16, January.

Also D. Corbae and P. D’Erasmo, (2019), Capital Requirements in a Quantitative Model of banking Industry Dynamics, NBER Working Paper Nr. 25424, January. They find that regulatory policies can have an important impact on market structure in the banking industry, which, along with selection effects, can generate changes in allocative efficiency.

They carried weights of 0% (e.g. cash, bullion, home country debt like Treasuries), 20% (securitizations such as mortgage-backed securities [MBS] with the highest AAA) rating 50%, and 100% (e.g. most corporate debt), with some assets given ‘No rating’. Banks with an international presence were required to hold capital equal to 8% of their risk-weighted assets (RWA). The 0% capital charge against Treasuries implies that a sudden increase in sovereign default risk may lead to liquidity issues in the banking sector and that capital requirements for government bonds increase the shock-absorbing capacity of the banking sector and thus the financial stability. See in detail: U. Neyer and A. Sterzel, (2017), Capital Requirements for Government Bonds. Implications for Bank Behaviour and Financial Stability, Düsseldorf University Press Working Paper Nr. 275, December. Bank capital charges trigger a different composition of credit on the balance sheet of banks. Even increases in capital ratio requirements can locally increase banks’ riskiness, due to the complexity of compositional changes, and the relations between prices and regulatory risk weights, and between interest rate pass-through and bank capital scarcity. See in detail: M. Harris et al., (2017), Bank Capital and the Composition of Credit, Working Paper, September 20, mimeo. On the other hand, there is evidence that higher capital requirements can increase bank lending. Government guarantees generate an implicit subsidy for banks. Even though a capital requirement reduces this subsidy, a bank may optimally respond to a higher capital requirement by increasing lending. This requires that the marginal loan generates positive residual cash flows. Since an increase in the capital requirement makes the bank safer, it makes the shareholders internalize such cash flows, known as the ‘forced safety effect’. See in detail: S. Bahaj and F. Malherbe, (2018), The Forced Safety Effect: How Higher Capital Requirements Can Increase Bank Lending, Working Paper, July 3, mimeo; higher capital requirements make secured lending more attractive vis-à-vis unsecured lending for the affected banks as secured loans require less regulatory capital. See: H. Degryse et al., (2018), To Ask or Not to Ask? Collateral versus Screening in Lending Relationships, Bank of Portugal Working Paper Nr. 19, Lisbon.

Capital requirements also have a dampening effect on the transmission of risk to the market and the cost of funding. See: A. Galiay and L. Maurin, (2015), Drivers of banks’ Cost of debt and Long-Term Benefits of Regulation- An Empirical Analysis Based on EU Banks, ECB Working Paper Series, Nr. 1849, September. See regarding the cyclicality of Capital Requirements: EBA, (2016), Cyclicality of Capital Requirements, EBA-Op-2016-24, December 22. They also have an effect on lending behavior by banks. See for details: H. Fraisse et al., (2017), The Real Effects of Bank Capital Requirements, ESRB Paper Nr. 47, June. Similar results have been observed following the introduction of the Basel III capital and liquidity requirements. See in detail: S.B. Naceur and C. Roulet, (2017), Basel III and Bank-Lending: Evidence from the United States and Europe, IMF Working Paper Nr. WP/17/245, November. Liquidity requirements tend to have a positive effect on lending behavior, but variations exist when comparing US and European effects. Naceur and Roulet further conclude that the ability of a bank to originate credit opportunities determines how they deal with the aforementioned requirements. Under circumstances they become ineffective or even detrimental. See also: R. De Young and K.Y. Yang, (2016), Do Banks Actively Manage their Liquidity, Journal for Banking and Finance, Vol. 66, PP.143–161; P.H. Kupiec et al., (2017), Does Bank Supervision Impact Bank Loan Growth?, Journal of Financial Stability, Vol. 28, pp. 21–48. Shan studied the relationship between credit risk in bank loan portfolios and systemic risk. Shan found that systemically risky banks originate new loans with greater default risk exposure even after controlling for self-selection of lenders by borrowers. See in detail: Y. Shan, (2017), Systemic Risk and Credit Risk in bank Loan Portfolios, Working Paper, November 3. Systemically risky banks take higher (lower) share allocations in loans borrowed by risky (safe) borrowers. This confirms the previous findings and indicates that the positive relationship between systemic risk and credit risk-taking is at least partially driven by supply-side factors. See also: Y. Shan, (2018), Systemic Risk, Credit Risk, and the Effect of managerial Style in Syndicated Bank Loans, Working Paper, January 13.

See also: F. Allen et al., (2017), Government Guarantees and Financial Stability, ECB Working Paper Nr. 2032, February. Guarantees are welfare improving because they induce banks to improve liquidity provision although in a way that sometimes increases the likelihood of runs or creates distortions in banks’ behavior. They further raise questions such as [a]re guarantees effective in preventing banking crises? What are the implications they have for banks’ role as Iiquidity providers and their risk-taking decisions? How do guarantees affect the interaction between liquidity provision and risk-taking? They build their model along the two-risk/crisis model for banks: insolvency risk and liquidity risk. They conclude that ‘the probability of both crises is endogenous and depends on the banks’ risk choice, as well as on the (type and size of) government guarantees.’ They assume two types of guarantees: a minimum repayment for depositors, so that panic-driven bank runs are mitigated and the standard deposit insurance scheme. The effect of the guarantees on the probability of a crisis is twofold: ‘[o]n the one hand, guarantees have a positive direct effect, since, by increasing depositors’ repayments, they reduce their incentive to withdraw early and thus, banks’ exposure to liquidity risk. On the other hand, they affect banks’ risk-taking decisions and, thus have a negative indirect effect on the probability of a banking crisis.’ (pp. 2–3). Also the direction in bank’ behaviors (moral hazard) due to the availability of guarantees was discussed. The guarantees avoid the need by banks to internalize externality costs as ‘the introduction of guarantees creates a wedge between the deposit rate chosen by banks and the one that the government would like to choose.’ However, Allen et al. create a caveat indicating that guarantees do not always lead to excessive risk-taking and therefore increased run sensitivity. Sometimes banks behave the opposite way, that is, in the direction of a social optimum and get exposure to less risk. Whether a bank behaves one way or the other depends on the mechanics of the guarantee. They comment ‘[t]he important detail is whether the government ends up paying depositors more in the case the bank ends up failing in the longer term for fundamental reasons or in case there is a run and the bank faces a shortage of liquidity. If the former holds, then the cost of a run from the point of view of banks is higher than from the point of the government and the banks choose to limit their exposure to run.’ (p. 3)

See in detail: F. Malherbe, (2015), Optimal Capital Requirements over the Business and Financial Cycles, ECB Working Paper, Nr. 1830, July. It requires a bit more context ‘On the one hand, more bank capital means that the banking sector can absorb more losses, which suggests that the banking sector could expand. But, on the other hand, there is a general equilibrium effect that dominates the loss absorbing effect. To see the intuition behind the general equilibrium effect, first consider a single (atomistic) bank that doubles its equity base. It should simply be allowed to double the size of its assets. However, if all banks in the economy double their equity base, and if they are allowed to double the size of their assets, this could double aggregate lending in the economy. Given diminishing returns to capital on the real side of the economy and given that banks have incentives to take on too much risk, this will decrease marginal returns to an extent that is far from optimal. In fact, the optimal policy is to let the banking sector expand, but less than proportionally, which corresponds to an increase in capital requirements and resonates with the notion of counter-cyclical capital buffers of Basel’ (p. 3). Overlooking this effect will lead to an exacerbation of economic fluctuations and enhanced systemic risk.

The four objectives of Basel II were: (1) ensuring that capital allocation is more risk sensitive, (2) enhanced disclosure requirements which will allow market participants to assess the capital adequacy of an institution, (3) ensure that credit risk, operational risk and market risk are quantified based on data and formal techniques and (4) an attempt to align economic and regulatory capital more closely to reduce the scope for regulatory arbitrage. Regarding the question if bank capital is risk sensitive and in what way, Ahnert et al. documented a model to study the risk sensitivity of capital regulation. With a perfect signal, capital requirements are risk sensitive and achieve the first-best levels of risk and intermediation: safer banks attract cheaper deposit funding and require less capital. With a noisy signal, risk-sensitive capital regulation can implement a separating equilibrium in which low-quality banks do not participate. We show that the degree of risk sensitivity is non-monotone in the precision of the signal and in investment characteristics. Without a signal, a leverage ratio still induces the efficient risk choice but leads to excessive or insufficient intermediation. With perfect and noisy signal they refer to the preciseness and accurateness as well as timeliness of the bank’s signal regarding the quality of the bank assets. See in detail: T. Ahnert et al., (2018), Should Bank capital Be Risk Sensitive, Bank of Canada Staff Working Paper Nr. 48, September.

Regulatory capital is the amount of capital that a bank or other financial institution has to hold as required by its financial regulator. This is usually expressed as a capital adequacy ratio of equity that must be held as a percentage of risk-weighted assets. The whole idea is that given the level of risk that the bank holds on its balance sheet, a certain level of capital should be adequate to absorb any potential losses that might occur from risk exposures or sudden devaluations of certain asset prices in order to stabilize the FI and keep it afloat.

Operational risk has always been ignored somewhat. However, these days operational risk accounts for 25% or more of the risk profile of (particularly the largest) banks. Another uncommon relationship is that between operational risk and regulatory arbitrage. Operational risk is broadly defined as the risk of a loss due to the failure of people or processes (i.e. all risks outside of the traditional credit and market risks. That relationship works as follows: ‘[w]eakness in regulation leads to incentives for banks to increase their overall risk and shift their risk taking to less regulated risk areas, and in particular increase their exposure to operational risk.’ (p. 1) So regulatory arbitrage contributed to the increase rise of these less-regulated risk streams such as operational risk. In particular ‘operational risk was unregulated prior to the crisis in the sense that banks were not required to hold additional equity capital to cushion against operational losses. This provided banks with an incentive to shift their risk profiles and take on unprecedented levels of operational risk and thus increase their overall risk exposure’ (pp. 1–2). See in detail: B. Clark and A. Ebrahim, (2017), Risk Shifting and Regulatory Arbitrage: Evidence from Operational Risk, Working paper, June 23, mimeo. Also: N. Boyson et al., (2016), Why Don’t all Banks Practice Regulatory Arbitrage? Evidence from Usage of Trust-Preferred Securities. The Review of Financial Studies Vol. 29, Issue 7, July pp. 1821–1859; A. Chernobai et al., (2016), Business Complexity and Risk Management: Evidence from Operational Risk Events in US Bank Holding Companies, Working paper, mimeo; Y. Demyanyk, and E. Loutskina, (2016), Mortgage Companies and Regulatory Arbitrage, Journal of Financial Economics, Vol. 122, pp. 328–351.

Since 2016 there are minimum capital requirements developed to match market risk. See: BCBS, (2016), Minimum Capital Requirements for Market Risk, Standards, January. They are under revision following a 2018 consultative update: BCBS, (2018), Revisions to the Minimum Capital Requirements for Market Risk, March. Final revisions were implemented through BCBS, (2019), Minimum Capital Requirements for Market Risk, February 25. There is an insurance market for market risk but not without issues: R. Koijen and M. Yogo, (2018), The Fragility of market Risk Insurance, NBER Working Paper Nr. 24182, May (revised).

The Internal Capital Adequacy Assessment Process (ICAAP) is the result of Pillar II of the Basel II accords.

To that effect the ESRB produces (since 2012) a quarterly risk dashboard, which includes de facto analysis of the following items: interlinkages, macro risk, credit risk, funding and liquidity risk, market risk, profitability and solvency risk, structural risk and central counterparty related risks (via www.​esrb.​europe.​eu).

M. Tanaka and J. Vourdas, (2018), Equity, Debt and Moral Hazard: the Optimal Structure of Banks’ Loss Absorbing Capacity, Bank of England Staff Working Paper Nr. 745, July 27. Banks in their model are subject to two types of moral hazard: i) ex ante, they have the incentive to shirk on project monitoring, thus increasing the risk of failure, and ii) ex post, poorly capitalized banks have the incentive to engage in asset substitution by ‘gambling for resurrection’. Ex ante moral hazard can be eliminated by ensuring that banks have sufficient capital and uninsured ‘bail-inable’ debt, while ex post moral hazard is mitigated by triggering resolution when the minimum capital requirement is breached. They argue that optimal regulation consists of a high TLAC requirement and high capital buffer. Their analysis also suggests that higher system-wide risk would call for a higher capital buffer. Direct lending by non-banks increases when local banks are poorly capitalized. Non-bank lenders are less likely to monitor by including financial covenants in their loans, but appear to engage in more ex ante screening. Controlling for firm and loan characteristics, non-bank loans carry about 200 basis points higher interest rates. In detail: S. Chernenko et al., (2018), Nonbank Lending, (2018), Fisher College of Business Working Paper Nr. 13, July 25.

See extensively: L. Nijs, (2014), Mezzanine Financing: Tools, Applications and Total Return, Wiley & Sons, London, in general but in particular the chapter on FIs.

Sir J. Cunliffe, (2016), Credit: Can Trees Grow to the Sky, Speech at the British Property Federation Annual Residential Investment Conference, London, February 9, bis.​org. The credit expansion we have witnessed in recent decades did not finance the economic expansion. Rather, the liquidity availability, combined with lowering interest rates, inflated prices.

Average leverage in other industries is around 35–45% of total funding and significantly up from two or three decades ago where it was only on average 20%. In the financial industry the leverage is often as high as 96% and before the crisis 98% of total funding. See in detail: A. Admati and M. Hellwig, (2013), The Bankers New Clothes: What’s Wrong with Banking and What to Do About It, Princeton University Press, Princeton, New Jersey, Chapter 1.

J. Danielsson, (2002), The Emperor has No Clothes: Limits to Risk Modeling, Journal of Banking and Finance, Vol. 26, pp. 1273–1296, J. Danielsson et al., (2016), Model Risk of Risk Models, Journal of Financial Stability, Volume 23, pp. 79–91; J. Danielsson et al., (2018), Learning from History: Volatility and Financial Crises, Review of Financial Studies, Vol. 31, Issue 7, July, pp. 2774–2805 and N.N. Taleb, (2010), The Black Swan: The Impact of the Highly Improbable, Random House Trade paperbacks, 2nd Ed., New York.

See in detail: Basel Committee on Banking Supervision (BIS), (2011), Basel III: A Global Regulatory Framework for More Resilient Banks and Banking Systems and Basel Committee on Banking Supervision, (2013), Basel III: The Liquidity Coverage Ratio and Liquidity Risk Monitoring Tools.

See FSB’s Basel III – Implementation via fsb.​org. Consider also the permanent updates the BIS provides clarifying many technical issues: BIS, (2017), Basel III: Finalizing Post-Crisis Reforms; BIS, (2017), Definition of Capital, Frequently Asked Questions; BIS, (2017), Frequently Asked Questions on Market Risk Capital Requirements; BIS, (2016), TLAC Holding Standard, October, (This document is the final standard on the regulatory capital treatment of banks’ investments in instruments that comprise total loss-absorbing capacity (TLAC) for global systemically important banks (G-SIBs). The standard aims to reduce the risk of contagion within the financial system should a G-SIB enter resolution. It applies to both G-SIBs and non-G-SIBs. The main elements of the prudential treatment are as follows: Tier 2 deduction: banks must deduct holdings of TLAC instruments that are not already included in regulatory capital from their own Tier 2 capital. Threshold below which no deduction is required: the deduction is subject to the thresholds that apply to existing holdings of regulatory capital and an additional 5% threshold for non-regulatory-capital TLAC holdings only. Instruments ranking pari passu with subordinated forms of TLAC must also be deducted. The standard also reflects changes to Basel III to specify how G-SIBs must take account of the TLAC requirement when calculating their regulatory capital buffers; BIS, (2016) Minimum Capital Requirements for Market Risk, January; BIS, (2015), Basel III: The Standardized Approach for Measuring Counterparty Credit Risk Exposures: FAQ, August, BIS, (2014), FAQs on Basel III’s January Liquidity Coverage Ratio; BIS, (2012), FAQs on Counterparty Credit Risk and Exposures to Central Counterparties and BIS, (2011), FAQs Regarding the Definition of Capital, and BIS, (2011), FAQs Regarding the Framework for Liquidity. S. Kroon and I. van Lelyveld, (2018), Counterparty Credit Risk and the Effectiveness of banking Regulation, DNB Working Paper nr. 599, June 26. The EBA launched recently a consultation on four RTSs with a view toward harmonization of the Standardised Approach for Counterparty Credit Risk (SA-CCR). See EBA, (2019), Consultation Paper on the Standardised Approach for Counterparty Credit Risk (abbr.), EBA consultation document EBA?CP/2018/03, May 2.

The original Capital Requirements Directive (CRD I) comprises Directives 2006/48/EC and 2006/49/EC. Capital Requirements Directive II (CRD II) comprises Directives 2009/27/EC, 2009/83/EC and 2009/111/EC. Capital Requirements Directive III (CRD III) comprises directive 2010/76/EC. CRD II and CRD III amended the Directives that comprise CRD I. The Basel III principles have been implemented through a Directive (Directive 2013/36/EU of the European Parliament and of the Council of June 26, 2013 on access to the activity of credit institutions and the prudential supervision of credit institutions and investment firms, amending Directive 2002/87/EC and repealing Directives 2006/48/EC and 2006/49/EC, OJ L 176, 27/06/2013, p. 338–436), which needed to be transposed in national law by January 1, 2014, and an aligned Regulation (Regulation (EU) No 575/2013 of the European Parliament and of the Council of June 26, 2013 on prudential requirements for credit institutions and investment firms and amending Regulation (EU) No 648/2012 Text with EEA relevance, OJ L 176, 27/06/2013, pp. 1–337). The CRD I, II and III were repealed on December 31, 2013. The Directive and Regulation are known as the CRD IV package.

For the international dimension: A. Malkhozov et al., (2017), International Illiquidity, International Finance Discussion Papers Nr. 1201, March.

See for an evaluation at great length: ESRB, (2015), A Review of Macro-Prudential Policy in the EU one year after the Introduction of the CRD/CRR, June, Frankfurt am Main.

This review is known as CRD V/CRR 2: Proposal for a Directive of the European Parliament and of the Council amending Directive 2013/36/EU as regards exempted entities, financial holding companies, mixed financial holding companies, remuneration, supervisory measures and powers and capital conservation measures; Proposal for a Regulation of the European Parliament and of the Council amending Regulation (EU) No 575/2013 as regards the leverage ratio, the net stable funding ratio, requirements for own funds and eligible liabilities, counterparty credit risk, market risk, exposures to central counterparties, exposures to collective investment undertakings, large exposures, reporting and disclosure requirements and amending Regulation (EU) No 648/2012COM(2016)850 & 854, 23.11.2016, 2016/0360,0364(COD). The aim is to implement the most recent international regulatory provisions for banks, set by the Basel Committee on Banking Supervision (BCBS), to address regulatory shortcomings and to contribute to more sustainable bank financing of the economy, especially regarding SMEs. Completion of the CRD V/CRR II package occurred in April 2019; via Europa.eu (MEMO/19/2129).

See regarding the consequences of overleverage on the financial sector and the real economy: M. Gross et al., (2017), Destabilizing Effects of Bank Overleveraging on Real Activity-An Analysis Based on a Threshold MCS-GVAR, ECB Working Paper Nr. 2081, June. They comment ‘the reason for why macroeconomic responses to bank capital shocks are likely significantly stronger under an overleveraging regime is that the same percentage point capital ratio shock translates into a stronger asset side reaction than under an initial low-leverage (i.e. high capital ratio) regime.’…‘GDP to credit long-run response ratios are higher under a bank overleveraging regime’…‘cross-border spillover effects appear to be more pronounced when capital ratio shocks hit the banking system during a period of overleveraging.’ (p. 15).

Also: F. Restoy, (2019), Proportionality in financial regulation: where do we go from here? Speech at the BIS/IMF policy implementation meeting on proportionality in financial regulation and supervision, Basel, Switzerland, 8 May, via bi.​org

Material recapitalization is required to have the effect sought after (increase lending, attract deposits and clean up balance sheets). See in detail: T. Homar, (2016), Bank Recapitalizations and Lending: A Little is not Enough, ESRB Working paper Series, Nr. 16, June.

See: A. Delivorias, Ranking of unsecured debt instruments in insolvency hierarchy, European Parliamentary Research Service, Briefing, March, 142017.

See a.o.: European Commission, Frequently Asked Questions: Capital requirements (CRR/CRD IV) and resolution framework (BRRD/SRM), amendments, MEMO 16/3840, Brussels, November 26, 2016. Note however already at this stage that the effectiveness of higher capital ratios and lower leverage ratios is continuously being questioned. To a large degree the findings are that rather than reduce bank risk, the risk is sifted because of regulatory intervention outside the ‘traditional’ banking sector. See, for instance, M. Allahrakha et al., (2016), Do Higher Capital Standards Always Reduce Bank Risk? The Impact of the Basel leverage Ratio on the U.S. Triparty Repo market, OFR Working Paper Nr. 16-11, November 10. Their findings with respect to BHCs (Bank Holding Companies cover all entities (US) that qualify as being a bank themselves or own an entity that does so for 10% or more) point in that direction. They examined the activity of broker-dealers affiliated with bank holding companies (BHCs) and broker-dealers not affiliated with BHCs in the repurchase agreement (repo) market. Their findings are threefold: (1) First, following the 2012 introduction of the supplementary leverage ratio (SLR), broker-dealer affiliates of BHCs decreased their repo borrowing but increased their use of repo backed by more price-volatile collateral; (2) they find that regardless of whether a US BHC-affiliated broker-dealer parent is above or below the SLR requirement, the announcement of the SLR rule has disincentivized those dealers affiliated with BHCs from borrowing in tri-party repo; and (3) they document the increase in the number of active non-bank-affiliated dealers in certain asset classes of tri-party repo since the 2012 introduction of the supplementary leverage ratio.

T. Goel, (2016), Banking Industry Dynamics and Size-Dependent Capital Regulation, BIS Working Paper Nr. 599, December. Bank leverage choices are subject to the risk-return trade-off: high leverage increases expected return on capital, but also increases return variance and bank failure risk.

See in detail also historically: M.A. Centeno and J.N. Cohen, (2012), The Arc of Neoliberalism, Annual Review of Sociology, Vol. 38, pp. 317–340.

See in detail: A. Greenspan, (2008), The Age of Turbulence: Adventures in a New World, Penguin Books, New York. After the 2008 crisis he converted and claimed that he has had to backtrack on his initial belief that (financial) markets can be self-policing and explicitly stepped away from the neoliberal policies he endorsed for decades.

Or to be precise (1) destruction does not always have to be creative and (2) not every creativity is destructive.

See for an excellent analysis of liberalization beyond just being a transition from controlled to competitive markets. Liberalization implies not merely practical processes and legal instruments of economic reorganization and governance, but moreover a higher-level conception of how markets fit within society, and thus how law might be deployed to achieve wider social and economic goals. See in detail: N. Dunne, (2017), Perspectives on Liberalisation, LSE Law, Society and Economy Working Papers Nr. 6/2017, London.

Much has been written about neoliberalism. But the reading of Wilkinson’s essay was refreshing; he coins the concept of ‘authoritarian liberalism’ and applies it to what he sees happening in the EU. Authoritarian liberalism occurs when politically authoritarian forms of governing emerge to protect the material order of economic liberalism. This constitutional conjuncture can be grasped by integrating into constitutional enquiry the material dynamic between democracy and capitalism. The post-Maastricht EU construction represents the de-democratization of the economy. The recent assaults on democracy in the Euro crisis appear to be a continuation of, rather than divergence from, the normal path of integration, Wilkinson indicates. See in detail: M.A. Wilkinson, (2018), Authoritarian Liberalism: The Conjuncture behind the Crisis, LSE Law, Society and Economy Working Papers 5/2018 (also published in The Crisis behind the Eurocrisis: The Eurocrisis as a Multidimensional Systemic Crisis of the EU, (eds. E. Nanopoulos and F. Vergis), June 2019, Cambridge University Press, Cambridge). See Epsein and Rhodes who argue in the same direction but with distinct accents. The European Banking Union and Capital Markets Union have emerged as two of the key pillars of European integration since the post-2008 financial crisis. Both imply the same critical shifts in Europe’s institutional political economy. The first relocates national oversight and authority to supranational institutions (a political shift), while the second increases the power and responsibility of market actors by reducing national controls (an economic shift). See in detail: R. Epstein and M. Rhodes, (2018), From Governance to Government: Banking Union, Capital Markets Union and the New EU, Competition and Change, Vol. 22 Nr. 2, pp. 205–224. Part of the migration to market-based but state-led finance is the role of public development banks which have become strategic players in the evolution of the European financial system and economic governance. ‘Market-based but state-led’ seems to be crucial in understanding neoliberalism. See: D. Mertens and M. Thiemann, (2018), Market-Based but State-Led: The Role of Public Development Banks in Shaping Market-Based Finance in the European Union, Competition and Change, Vol. 22, Nr. 2, pp. 184–204.

L. Nijs, (2015), Neoliberalism 2.0. Regulating and Financing Globalizing Markets, Palgrave, Basingstoke, chapter 3.

In the City of London, the financial sector accounted for 40% of all UK corporate income tax revenues in the years running up to the crisis. This has fallen but its corporate tax contributions still account for about 10% of all UK tax revenues (2010) and far beyond that taking into account VAT and so on. For details see: PWC, (2010), The Total Tax Contribution of UK Financial Services, 3rd ed., London. By 2017 that percentage is still at a stable 11% PWC, (2017), Ibid. 10th ed. The study is updated annually.

In fact that is true for the wider sovereign in all its aspects including the DOJ (‘Department of Justice’). See in detail: B.L. Garrett, (2014), Too Big to Jail: How Prosecutors Compromise with Corporations, Harvard University Press, Cambridge MA.

Countless examples can be given regarding the predatory nature of the FI: From the libor manipulation to FIs taking trading positions against their clients, under-highlighting certain risks, to driving SME clients into bankruptcy/insolvency in order to pick up quality assets at bottom prices.

That is the case for most Western nations.

See B. Őztürk and M. Mrkaic, (2014), SME’s Access to Finance in the Euro Area: What Helps or Hampers, IMF Working Paper, WP 14/78; R. Bannerjee, (2014), SME’s, Financial Constraints and Growth, BIS Working Paper nr. 475. H. Kraemer-Eis, (2017), SME Securitization in Europe- a Short Summary, EIF report and H. Kraemer-Eis et al., (2015), SME Securitizations- At a Crossroads, EIF Working Paper Nr. 2015/31. Kraemer-Eis advocates a securitization market for SME loans that would provide an indirect access to capital markets. He mainly finds support with Altunbas et al. (2007).

Securitization per se is not good or bad—it is a toolbox, it is value-free, claims Kraemer-Eis. Unfortunately, that claim violates reality. Reference can be to the concentration of risk discussion in tranching.

And less capital required means under constant performance a higher return on equity (ROE) for the FI.

See for a recent literature review: A. Mordel, (2018), Prudential Liquidity Regulation in Banking- A Literature Review, Bank of Canada Staff Working Paper Nr. 8, July. His findings suggest that while banks respond to binding requirements by increasing long-term funding and reducing maturity mismatch, there is also evidence that risk in the financial system has gone up. In an environment where both bank liquidity and capital are regulated, it is natural to consider the interactions between them. The main conclusions from this growing literature indicate that while liquidity requirements tend to make capital constraints less binding, capital requirements appear to be more costly to comply with, and that both regulations have a non-trivial effect on financial stability.

See in detail: C. Bonner, (2014), Preferential Regulatory Treatment and Bank’s Demand for Government Bonds, DNB Working Papers, Nr. 433. It also creates more stability: M. Bucher et al., (2019), More Stability through Liquidity Regulation, Deutsche Bundesbank Research Brief Nr. 25, May.

For the reason why banks focus more on ROE than, for instance, EPS like other firms, see: G. Pennacchi and J.A.C. Santos, (2018), Why Do Banks Target ROE?, FRB of NY Staff Report Nr. 855, June. Also A. Kovner and P. van Tassel, (2019), Evaluating Regulatory Reform: Banks’ Cost of Capital and Lending, FRB of NY Staff Report, 854, May.

In the EU the LTRO (Long-Term Refinancing Operation) and in the US the scaled quantitative easing (QE) program were the major programs initiated.

Also in the post-2008 world the EMH (efficient market hypothesis), which assumes that markets are constantly rational and therefore price assets (and their embedded risk) appropriately do not stand.

And the other way around. See: T. Jonasson and M. Papaioannou, (2018), Primer on Managing Sovereign Debt-Portfolio Risks, IMF Working Paper Nr. WP/18/74, April.

See for an excellent review of the impact of credit on recessions: J. Bridges et al., (2017), Down in the Slumps: the Role of Credit in Five Decades of Recessions, Bank of England Staff Working Papers Nr. 659, April. They assess whether the growth or level of credit is the better predictor of the severity of a recession. They concluded that ‘a period of rapid credit growth in the immediate run-up to a recession predicts a deeper and longer downturn than when credit growth has been subdued, whether associated with a systemic banking crisis or not and whether that credit growth reflects borrowing by households or businesses. Credit growth is a more statistically and economically significant predictor of a recession’s severity than the level of indebtedness.’ Also: J. Cunliffe, (2016), Credit: Can Trees Grow to the Sky, Speech given by Sir Jon Cunliffe, British Property Federation Annual Residential Investment Conference, London, February 9 (bankofengland.​co.​uk).

About the predictability (through early warning systems) of fiscal crises see: S. Cerovic et al., (2018), Predicting Fiscal Crises, IMF Working Paper Nr. WP/18/181, August. Both non-fiscal (external and internal imbalances) and fiscal variables help predict crises among advanced and emerging economies. Also: M. Bruns and T. Poghosyan, (2018), Leading Indicators of Fiscal Distress: Evidence from the Extreme Bound Analysis, Applied Economics, Vol. 50, Issue 13, pp. 1454–1478; M. Dawood et al., (2017), Predicting Sovereign Debt Crises: An Early Warning System approach, Journal of Financial Stability, Vol. 28, pp. 16–28; C. Christofides et al., (2016), Did established Early Warning Signals Predict the 2008 Crises?, European Economic review, Vol. 81, pp. 103–114; K. Gerling et al., (2017), Fiscal Crises, IMF Working Paper Nr. WP/17/86, Washington; S. Sumner and K. Berti, (2017), A Complementary Tool to Monitor Fiscal Stress in European Economies, EC Discussion Paper Nr. 49, June. Honda et al. assess the options of waiting for early warning signals to tackle fiscal crises or handle as they emerge. They investigate the interlinkages between early warning signals for fiscal crisis, policy responses and policy outcomes. See: J. Honda et al., (2018), When Do We Repair the Roof? Insights from Responses to Fiscal Crisis Early Warning Signals, IMF Working Paper Nr. WP/18/77, March. The bottom line is that countries with weak institutional environments benefit from acting on early warning signals whereas strong institutional environments are able and willing to introduce fiscal adjustments when needed (at the beginning of a fiscal crisis) and can afford as they tend to have sufficient buffers. Substitutability between fiscal consolidation and institutional quality (in order to prevent a fiscal crisis) points to the importance of capacity (resilience) building. Also: M.G. Attanasi and L. Metelli, (2017), Is Fiscal Consolidation Self-Defeating? A Panel-VAR Analysis for the Euro Area Countries, Journal of International Money and Finance, Vol. 74(C), pp. 147–164; M. Bruns and T. Poghosyan, (2016), Leading Indicators of Fiscal Distress: Evidence from the Extreme Bound Analysis. IMF Working Paper Nr. WP/16/28; K. Gerling et al., (2017), Fiscal Cries, IMF Working Paper Nr. WP/17/86; S. Cerovic, et al., (2018), Predicting Fiscal Crisis, IMF Working Paper Nr. WP/18/181, August.

Distinction needs to be made between the mentioned bank bailout and other techniques of converting private sector debt into public sector debt. This can be through a crisis or a more orderly deleveraging process. This debt migration ‘operates mainly through growth rather than explicit bailouts: private deleveraging weighs on activity, prompting a countercyclical government response to support economic activity. Whenever the private sector is caught in a debt overhang and needs to deleverage, governments systematically come to the rescue through a countercyclical rise in government deficits and debt’ (p. 3). It is a form of debt mutualization whereby excess private debt leads to higher public debt. The transmission channel is not debt but growth. ‘Private deleveraging weighs on economic activity, thereby prompting both a cyclical deterioration in public finances and a countercyclical rise in public debt. Ultimately, whether this debt substitution results in a net increase or a net decline of overall indebtedness in the economy depends on the extent of the growth slowdown during the deleveraging spell’ indicate Mbaye et al. Treating public and private debt in silos should be replaced by assessing the total stock of debt active in the economy. Unchecked private debt is causally related to public sector debt beyond bank bailout programs. See in detail: S. Mbaye et al., (2018), Bailing Out the People? When Private Debt Becomes Public, IMF Working Paper Nr. WP/18?141, June. Also: A. Alter et al., (2018), Understanding the Macro-Financial Effects of Household Debt: A Global Perspective, IMF Working Paper Nr. WP/18/76, International Monetary Fund, Washington, DC.; M. Bernardini and L. Forni, (2017), Private and Public Debt: Are Emerging Markets at Risk, IMF Working Paper Nr. WP/17/61, International Monetary Fund, Washington, DC.; A. Chudik et al., (2017), Is there a Debt-Threshold Effect on Output Growth?, Review of Economics and Statistics, Vol. 99, Issue 1, pp. 135–150; I. Hasan et al., (2016), What Type of Finance Matters for Growth? Bayesian Model Averaging Evidence, Policy Research Working Paper Series Nr. 7645, The World Bank; MS. Mbaye et al., (2018), Global Debt Database: Methodology and Sources, IMF Working Paper Nr. WP/18/111, International Monetary Fund, Washington, DC., May. An interesting read is B. Eichengreen et al., (2019), Public Debt Through the Ages, IMF Working Paper Nr. WP/19/6, January. They observe that the purposes for which governments borrow have evolved over time. Both periods of debt expansion as well as debt consolidation are analyzed, and the economic and political circumstances that made these successful debt consolidation episodes possible are closely examined. Highly recommended including the extensive reference list (pp. 51 ff).

L. Grégory et al., (2019), The Cost of Banking Crises: Does the Policy Framework Matter, Banque de France Working Paper Nr. 712, March 13. They highlight that extremely restrictive policy frameworks are likely to increase the expected cost of banking crises.

Kandrac and Schlusche argue that the ‘affected institutions (ed. those subject to reduced or no bank supervision) took on much more risk than their unaffected counterparts elsewhere that were subject to identical regulation.’ There seems to be some fading out effect over time of that behavior. See: J. Kandrac and B. Schlusche, (2017), The Effect of Bank Supervision on Risk Taking: Evidence from a Natural Experiment, Board of Governors of the Federal Reserve System Working Paper, May 31, mimeo.

Cross-border transmission of fiscal shocks is stronger when monetary policy is constrained (i.e. after a banking crisis). See P. Blagrave et al., (2018), Cross-Border Transmission of Fiscal Shocks: The Role of Monetary Conditions, IMF Working Paper Nr. WP/18/103, May. Also: A. Goujard, (2017), Cross-Country Spillovers from Fiscal Consolidations, Fiscal Studies, Vol. 38, Issue 2, pp. 219–267; S.B. Nicar, (2015), International Spillovers from U.S. Fiscal Policy Shocks, Open Economies Review, Vol. 26, Issue 5, pp. 1081–1097; V.A. Ramey et al., (2018), Government Spending Multipliers in Good Times and in Bad: Evidence from U.S. Historical Data, Journal of Political Economy, Vol. 126, Issue 2, pp. 850–901.

D. Amaglobeli et al., (2015), From Systemic Banking Crisis to Fiscal Costs: Risk Factors, IMF Working Paper Nr. WP/15/166, p. 3; ‘Most of these are salient for both direct and overall fiscal costs, but policy responses have a differential impact. For example, bank guarantees appear to increase both direct and overall fiscal costs; the correlation is less clear-cut though for other policy measures, such as recapitalizations and asset purchases. Even though these short-term measures have initial direct fiscal costs, they do not necessarily add to the overall fiscal cost of crises. These findings suggest a possible trade-off between costly short-term policy interventions and the overall increase in public debt (p. 3)’.

In fact the link between the sovereign and the economy within that sovereign goes beyond the FI sector. There is a material relationship between the sovereign position and the spillover into corporate credit risk. This is extensively documented using the Greek scenario as a test-case. See in detail: P. Augustin et al., (2016), Sovereign to Corporate Risk Spillovers, ECB Working Paper Series Nr. 1878, January. The sovereign to corporate channels identified are: (10 Corporate income taxation, (2) possible expropriation, (3) the sovereign ceiling concept, (4) downsizing of public investments and consumption and (5) reduced corporate credit of firms that rely on public spending can spread to suppliers. Also Breckenfelder and Schwaab identified spillover effects to that effect: H.-J. Breckenfelder and B. Schwaab, (2015), The Bank-Sovereign Nexus across Borders, Working Paper mimeo and more recently H.-J. Breckenfelder and B. Schwaab, (2017), Bank to Sovereign Risk Spillovers across Borders: Evidence from the ECB’s Comprehensive Assessment, Working Paper, November 30, mimeo; G. Dell’Ariccia et al., (2018), Managing the Sovereign-Bank Nexus, ECB Working Papers Nr. 2177, September 24. The latter paper identifies the various channels that give rise to a “sovereign-bank nexus” whereby the financial health of banks and sovereigns is intertwined. They find that banks and sovereigns are linked by three interacting channels: banks hold large amounts of sovereign debt; banks are protected by government guarantees; and the health of banks and governments affect and is affected by economic activity. Evidence suggests that all three channels are relevant. Although there is a certain commitment to break the nexus between banks and the sovereign, there is less consensus how that is about to be made to happen, argue Alogoskoufis and Langfield. In particular the question regarding whether and how to reform the regulatory treatment of banks’ sovereign exposures is left open. Simulations highlight a tension in regulatory design between concentration and credit risk. An area-wide low-risk asset—created by pooling and tranching cross-border portfolios of government debt securities— would resolve this tension by expanding the portfolio opportunity set. Banks could therefore reinvest into an asset that has both low concentration and low credit risk. He thereby apparently refers to the SBBS idea that was proposed in the same period. That discussion will be had in the section of safe assets, where many valid objections are tabled.

C. Arellano et al., (2017), Sovereign Risk Contagion, Federal Reserve Bank of Minneapolis, Staff Report Nr. 559, November (also NBER Working Paper Nr. 24031, November). Also: A. Ari, (2017), Sovereign Risk and Bank-Risk Taking, IMF Working Paper Nr. WP/17/280, December; V.V. Acharya et al., (2016), Caught Between Scylla and Charybdis? Regulating Bank Leverage when there is Rent Seeking and Risk Shifting, The Review of Corporate Finance Studies, Vol. 5, Issue 1, pp. 36–75; L. Boccola, (2016), The Pass-Through of Sovereign Risk, Journal of Political Economy, Vol. 124, Issue 4, pp. 879–926; M.K. Brunnermeyer et al., (2016), The Sovereign-Bank Diabolic Loop and Esbies, American Economic Review, Vol. 106, Issue 5, pp. 508–512; F. de Marco, (2017), Bank Lending and the European Sovereign Debt Crisis, Oesterreichische Nationalbank Working Paper Nr. 213; E. Farhi and J. Tirole, (2018), Deadly Embrace: Sovereign and Financial Balance Sheets Doom Loops, Review of Economic Studies, Vol. 85, Issue 3, July 1, pp. 1781–1823.

See for an extensive coverage on the matter and proposals to mitigate systemic risks: IMF, (2015), From Banking to Sovereign Stress: Implications for Public Debt, March, Washington D.C. Also: A. Pienkowski, (2017), Debt Limits and the Structure of Public Debt, IMF Working Paper Nr. WP/17/117, May. Pienkowski provides a framework for how to assess the structure of debt instruments that can raise the debt limit of a sovereign. Optimal instrument design is not a one-size-fits-all position.

See for an interesting overview of central banking models around the world: A. Khan, (2017), Central Bank legal Frameworks in the Aftermath of the Global Financial Crisis, IMF Working Paper Nr. WP/17/101, May.

See in extenso: R. Mohan and M. Kapur, (2014), Monetary Policy Coordination and the Role of Central Banks, IMF Working Paper, WP 14/70.

See in detail F. Lambert and K. Ueda, (2014), The Effects of Unconventional Monetary Policy on Bank Soundness, IMF Working Paper WP/14/152. Through the mechanism of globalization, this phenomenon has resulted not only in FIs being impacted by those policies, but also the clients they serve, including in emerging economies where the economic infrastructure overall is more susceptible to shocks; see M. Chui, I. Fender and V. Sushko, (2014), Risks Related to EME Corporate Balance Sheets: The Role of Leverage and Currency Mismatch, BIS Quarterly Review (September), pp. 35–47. See also: F. de Graeve and J. Lindé, (2015), Effects of Unconventional Monetary Policy, Sveriges Riskbank Economic Review Nr. 2015/1, pp. 43–74. Unconventional monetary policy and the low interest rate seem to reduce bank profitability in the long run; see: C. Borio et al., (2017), The Influence of Monetary Policy on Bank Profitability, International Finance Vol. 20, Issue 1, pp. 48–63. Bank profitability also impacts financial stability and that is what interests us here. Xu et al. made an attempt to formulate a model for analysis: in short they first developed a theoretical model of the relationship between bank profitability and financial stability by exploring the role of non-interest income and retail-oriented business models. Following that they identified the determinants of bank risks and profitability, and how the level and the source of bank profitability affect risks. Their conclusions can be summarized as being that ‘[r]esults reveal that profitability is negatively associated with both a bank’s contribution to systemic risk and its idiosyncratic risk, and an over-reliance on non-interest income, wholesale funding and leverage is associated with higher risks. Low competition is associated with low idiosyncratic risk but a high contribution to systemic risk. Lastly, the problem loans ratio and the cost-to-income ratio are found to be key factors that influence bank profitability.’ That implies that the link between bank profitability and stability risk all depends on the sources of bank profitability, in particular where there is an over-reliance on market-based non-interest income, leverage, and wholesale funding. See: T.T. Xu et al., (2019), Bank Profitability and Financial Stability, IMF Working Paper Nr. WP/19/5, January. Also: P. Abedifar, et al. (2018), Non-Interest Income and Bank Lending, Journal of Banking and Finance Vol. 87 (February), pp. 411–426; V.V. Acharya, et al. (2017), Measuring Systemic Risk, The Review of Financial Studies Vol. 30, Issue 1, pp. 2–47; G. Dell’Ariccia, et al. (2017), Bank Leverage and Monetary Policy’s Risk-Taking Channel: Evidence from the United States, Journal of Finance Vol. 72, Issue 2, pp. 613–654; I. Drechsler, et al. (2017), The Deposits Channel of Monetary Policy, Quarterly Journal of Economics Vol. 132, Issue 4, pp. 1819–1876.

If one assumes that the monetary toolkit is supposed to lead one way or the other to a Pareto optimum, one has concluded that (un)conventional monetary policies and prudential tools, capital requirements and the interest rate are not independent instruments, and no choice in itself will lead to that Pareto optimum. Extending the monetary toolkit with additional features (e.g. the payment of interest on bank reserves and QE policies) can in conjunction with the aforementioned instruments restore the Pareto optimum or approach that optimum. See in detail: M. Magill et al., (2016), Unconventional Monetary Policy and the Safety of the Banking System, USC Dornsife Institute for New Economic Thinking, Working Paper, Nr. 17-04, November 11.

Haldane et al. conclude that, besides the fact that only when central bank balance sheet expansions are used as a monetary policy tool they have a significant macroeconomic impact, there is evidence for the US that the effectiveness of QE may vary over time, depending on the state of the economy and liquidity of the financial system. QE can however also have strong spillover effects cross-border, mainly via financial channels. See in detail: A.G. Haldane et al., (2016), QE: The Story so far, Bank of England Working Paper, Nr. 624, October.

Much has been said and written about the optimal level and effectiveness of the QE programs worldwide. The same holds true about a possible QE exit which is proven to be very difficult. A permanent QE model however is never welfare improving, concludes Harrison. See in detail: R. Harrison, (2017), optimal Quantitative Easing, bank of England Staff Working Paper Nr. 678, September 22.

W. Buiter, (2008), Quantitative Easing and Qualitative Easing: A Terminological and Taxonomic Proposal, Financial Times, Willem Buiter’s mavercon blog. See more recent: H. Kuroda, (2017), Quantitative and Qualitative Monetary Easing and Economic Theory, Speech by the BoJ Governor at the University of Zurich, November 13. ‘Quantitative easing’ inflates the Central Bank balance sheet, printing money and adding liquidity to the system while qualitative easing modifies the asset composition. With qualitative easing, Central Banks absorb the risk, flattening the yield curve. Both measures increase inflation and reduce borrowing risk premiums with an impact on company’s balance sheet, widening economic and financial margins and decreasing the real value of debt’. See in detail: R.M. Visconti and M.C. Quirici, (2015), Qualitative Easing and Risk Transfer from Corporations to Central banks, Corporate Ownership and Control, Vol. 12, Issue 3, pp. 201–210. Ferrero et al. conclude that large-scale asset purchases by central banks have progressively reduced the slope of the risk-free yield curve, thus lowering banks’ net interest margin. This could have induced banks to extend credit to riskier borrowers, increasing concerns about financial stability. A flattening of the yield curve is associated with a reduction in the share of loans with a higher counterparty risk. Monetary policy measures that aim to reduce long-term rates are, therefore, able to stimulate economic activity without increasing banks’ credit risk. In detail: G. Ferrero et al., (2019), Credit Risk-Taking and Maturity Mismatch: the Role of the Yield Curve, Bank of Italy Working Paper Nr. 1220, April 29.

See: R.E.A. Farmer and P. Zabczyk, (2016), The Theory of Unconventional Monetary Policy, Bank of England Staff Working Paper Nr. 613, September. Their conclusion is twofold: a central bank that takes risk onto its balance sheet can increase welfare and the optimal intervention restores efficiency and is self-financing.

The follow-up question that emerges after the question about the effectiveness of UMP has been asked is the impact on the global economic fabric of the unwinding of such an UMP position. That unwinding has essentially two dimensions: changes (increases) in short-term policy rates and balance sheet adjustments. That will impact in particular emerging countries whose currency is pegged to the USD and those that have seen massive inflows from advanced economies. Mitigating the spillovers of those unwinding is very difficult, especially as most emerging economies use only short-term rates as a single monetary tool. Capital control and macroprudential policies will be needed to complement the financial stability toolkit. Balance sheet adjustments of central banks tend to impact long-term rates and will indirectly influence short-term rates through what is called ‘financial plumbing’. That occurs continuously in financial markets through posting and lifting collateral and which are ‘generally received by the collateral desks of the banks not only via reverse-repo but also from securities borrowing, prime brokerage agreements, and over-the-counter (OTC) derivative positions (p. 21)’. Those pledged securities are generally posted mark-to-market. The collateral re-use rate (or collateral velocity) has declined significantly since the crisis (from about 3% to 1.6%), partly due to the incoming legislation on this front. That decline in the re-use rate impacts financial lubrication. Now when the unwinding of central banks’ balance sheets (which cause good collateral to be released) goes hand-in-hand with the discussed declining re-use rate, short-term money rate might move away from policy rates. See in detail: M. Singh and H. Wang, (2017), Central bank Balance Sheet Policies and Spillovers to Emerging Markets, IMF Working Paper Nr. WP/17/172, July.

D. Quint and M.S.M. Peria, (2017), Should Unconventional Monetary Policies Become Conventional, IMF Working Paper Nr. WP/17/85, March. They conclude that the benefits of using such UMP in normal times are substantial, but that ‘the benefits from using UMP are shock-dependent and mostly arise when the economy is hit by financial shocks. When more traditional business cycle shocks (such as supply and demand shocks) hit the economy, the benefits of using UMP are negligible or zero’.

Often political models and the vested interests obstruct efficient interventions. One could argue that less interventionist policy makers would engage a Pigovian instrument faster as a Pareto dominated instrument and consequently act welfare enhancing. See: D. Austen-Smith et al., (2018), Gridlock and Inefficient Policy Instruments, Working Paper, June 7, mimeo.

To begin with the low interest rate environment presents a significant challenge to banks and is likely to ‘entail major changes to their business models over the long-run. Lower returns to maturity transformation in the face of flatter yield curves and an inability to offer deposit rates significantly below zero combine to compress bank earnings in this environment’, argue Q. Chen et al. Interestingly enough and in my understanding quite correctly they see a new equilibrium in that situation. Not driven solely by monetary efforts but driven by population aging and slower productivity growth, they see banking drifting toward provision of fee-based, utility services. See in detail: Q. Chen et al., (2018), Banking in a Steady State of Low Growth and Interest Rates, IMF Working Paper Nr. WP/18/192, August. Also: S. Claessens et al., (2017), Low-For-Long Interest Rates and Banks’ Interest Margins and Profitability: Cross-Country Evidence, International Finance Discussion Papers Nr. 1197, Board of Governors of the Federal Reserve System, Washington D.C.; G. Dell’ Arriccia et al., (2017), Bank Leverage and Monetary Policy’s Risk-Taking Channel: Evidence from the United States, Journal of Finance, Vol. 72, Nr. 2, pp. 613–654; I. Dresschler et al., (2017), The Deposits Channel of Monetary Policy, Quarterly Journal of Economics, Vol. 132, Nr. 4, pp. 1819–1876; M. Katagiri, (2018), The Equilibrium Yield Curve, the Phillips Curve and Monetary Policy, IMF Working Paper Nr. WP/18/242, November.

In detail: R. Agarwal and M. Kimball, (2019), Enabling Deep Negative Rates to Fight Recessions: A Guide, IMF Working Paper Nr. WP/19/84, April. The zero lower bound interest rate is not a law of nature, it is a policy choice. But monetary policy remains constrained by the lower bound in many countries, and thus limiting the options to address future deflationary shocks. The existence of cash prevents central banks from cutting interest rates much below zero. Assenmacher and Krogstrup now suggest decoupling cash from electronic money to achieve a negative yield on cash, which would remove the lower bound constraint on monetary policy. See in detail: K. Assenmacher and S. Krogstrup, (2018), Monetary Policy with Negative Interest Rates: Decoupling Cash from Electronic Money, IMF Working Paper Series Nr. WP/18/191, August. Also: G.B. Eggertson et al., (2019), Negative Nominal Interest Rates and the Bank Lending Channel, NBER Working Paper Nr. 25416, January. The latter document that deposit rates stopped responding to policy rates once they went negative and that bank lending rates in some cases increased rather than decreased in response to policy rate cuts. They construct a macro-model with a banking sector that links together policy rates, deposit rates and lending rates. Once the policy rate turns negative, the usual transmission mechanism of monetary policy through the bank sector breaks down. Moreover, because a negative policy rate reduces bank profits, the total effect on aggregate output can be contractionary.

This result holds after controlling for business and financial cycle conditions and different bank-specific characteristics, such as liquidity, capitalization, funding costs, risk and income diversification. See in detail: C. Borio and L. Gambacorta, (2016), Monetary Policy and Bank Lending in a Low Interest Rate Environment: Diminishing Effectiveness?, BIS Working Paper Nr. 612, February. Also: M. Bech, and A. Malkhozov, (2016): How have Central Banks Implemented Negative Policy Rates?, BIS Quarterly Review, March, pp. 31–44.

L. Gambacorta et al., (2017), Changing Business Models in International Bank Funding, BIS Working Paper Nr. 614, March.

See: P. Lainà, (2015), Money Creation under Full Reserve banking: A Stock-flow Consistent Model, Levy Economics Institute, Working Paper Nr. 851, October.

See for the issues with the current regulatory framework and some alternatives suggested: C. Fullenkamp and C. Rochon, (2014), Reconsidering Bank Capital Regulation: A New Combination of Rules, Regulators and Market Discipline, IMF Working Paper, Nr. WP/14/169.

Fullenkamp and Rochon, Ibid. pp. 4–9. Higher capital requirements seem to trigger higher risk taking in particular by large and less profitable banks, ESRB, (2019), E. Dautović, (2019), Has Regulatory Capital Made Banks Safer? Skin in the Game v. Moral Hazard, ESRB Working Paper Nr. Nr. 91.

V. Acharya et al., (2013), Testing Macroprudential Stress Tests: The Risk of Regulatory Risk Weights, NYU Stern, Working Paper. They show that the risk measures used in risk-weighted assets are cross-sectionally uncorrelated with market measures of risk as they do not account for the ‘risk that risk will change’. Furthermore, the firms that appeared to be best capitalized relative to risk-weighted assets were no better than the rest when the European economy deteriorated into the sovereign debt crisis in 2011. Also: R. Anderson et al., (2018), Macroprudential Stress Tests and Policies: Searching for Robust and Implementable Frameworks, IMF Working Paper Nr. WP/18/197, September. Other elements play a role, for example the (il)liquidity of the assets held by the bank; see: H. Tomura, (2014), Asset Illiquidity and Dynamic Bank Capital Requirements, International Journal of Central Banking, Vol. 10, Issue 3, pp. 291–317; L. Black et al., (2017), The Systemic Risk of European Banks During the Financial and Sovereign Debt Crises, FRB International Finance Discussion Paper Nr. 1083. Cetine et al. demonstrate the microprudential premise and limited view of systemic risk in supervisory stress tests. They find that indirect effects of a potential default, through the bank’ other counterparties, are larger than the direct impact on the bank. Also, taken as a whole, the core banking system has a higher concentration to a single counterparty than does any individual bank holding company (i.e. stress-testing model underestimates exposures). See in detail: J. Cetina et al., (2016), Stressed to the Core: Counterparty Concentrations and Systemic Losses in CDS Markets, OFR Working Paper Nr. 16-01, March 8. Interesting, in an EU context, is the question whether stress testing and its underlying assessment are different at a national versus EU level. How is the contribution of a bank to systemic risk validated and whether the drivers of systemic risk differ at the national and at the euro area level? Buch et al. conclude that ‘[w]hile the qualitative determinants of systemic risk are similar at the national and euro-area level, the quantitative importance of some determinants differs. For example, banks with a higher loan share contribute less to systemic risk, but this effect is stronger at the national level compared to the euro-area level.’ See in detail: C.M. Buch, et al., (2017), Drivers of Systemic Risk: Do National and European Perspectives Differ?, Deutsche Bundesbank Discussion Paper Nr. 9, Frankfurt am Main. Also: E. Jokivuolle et al., (2018), Testing the Systemic Risk Differences in Banks, Bank of Finland Research Discussion Paper Nr. 13, Helsinki.

A. Elbourne and K. Ji, (2019), Do Zero and Sign Restricted SVARs Identify Unconventional Monetary Policy Shocks in the Euro Area? CPB Discussion Paper, February 19, via cpb.nl.

Their models can be found here: P. Burriel, and A. Galesi, (2018), Uncovering the Heterogeneous Effects of ECB Unconventional Monetary Policies across Euro Area Countries, European Economic Review, Vol. 101(C), pp. 210–229; J. Boeckx, et al., (2017), Effectiveness and Transmission of the ECB’s Balance Sheet Policies. International Journal of Central Banking, Vol. 13(1), pp. 297–333; L. Gambacorta, (2014), The Effectiveness of Unconventional Monetary Policy at the Zero Lower Bound: A Cross-Country Analysis. Journal of Money, Credit and Banking, Vol. 46(4), pp. 615–642.

M. Jarocinski, and P. Karadi, (2018), Deconstructing Monetary Policy Surprises: the Role of Information Shocks, Working Paper Series Nr. 2133, European Central Bank, Frankfurt.

A. Elbourne et al., (2018), The Effects of Unconventional Monetary Policy in the Euro Area, CPB Discussion Paper Nr. 371, February 6.

Also: G. Corsetti, et al., (2018), One Money, Many Markets - A Factor Model Approach to Monetary Policy in the Euro Area with High-Frequency Identification, Cambridge Working Papers in Economics Nr. 1816, Faculty of Economics, University of Cambridge.

See for an overview of FS taxation in the EU: European Commission, (2010), Financial Sector Taxation, EC Taxation Papers Series, Working Paper Nr. 25.

In that triangle variables like the current account, real GDP, the fiscal balance and private sector credit play a role.

See in extenso: S. Basu et al., (2017), A Model to Assess the Probabilities of Growth, Fiscal, and Financial Crises, IMF Working Paper Nr. 17/282, December. Also: J. Abad et al., (2017), Mapping the Interconnectedness between E.U. Banks and Shadow Banking Entities, NBER Working Paper Nr. 23280; L. Alessi and C. Detken, (2017), Identifying Excessive Credit Growth and Leverage, Journal of Financial Stability, Vol. 35, April, pp. 215–225; L. Catão and G.M. Melisi-Ferreti, (2014), External Liabilities and Crises, Journal of International Economics, Vol. 94, Issue 1, pp. 18–32.

Besides the fact that countries are losing control of their domestic financial conditions. See in detail: IMF, (2017), Are Countries Losing Control of Domestic Financial Conditions, Global Financial Stability Report, Getting the Policy Mix Right, April, pp. 83–107. They argue ‘[g]reater financial integration can complicate the management of domestic financial conditions in several ways. First, policymakers may need to take external factors into greater consideration when pursuing domestic objectives. Second, global financial integration may make it harder for domestic policymakers to control financial conditions at home—for example, it may hamper the transmission of monetary policy’ (p. 83). Examplary can be further referred to: L. Catão et al., (2017), International Financial Integration and Funding Risks: Bank-Level Evidence from Latin America Prepared, IMF Working Paper Nr. WP/17/224, October. They draw three main conclusions: (1) international financial liberalization lowers bank capital ratios and increases the shares of short-term funding; (2) large banks substitute interbank borrowing for equity and long-term funding, whereas small banks increase the proportions of retail funding in their liabilities; and (3) riskier bank funding in the aftermath of financial liberalizations is exacerbated by asymmetric information, which rises on geographical distance and the opacity of balance sheets. Also: D. Duffie and A. Krishnamurthy, (2016), Pass-Through Efficiency in the Fed’s New Monetary Policy Setting, Stanford University Working Paper. Mimeo; S. Avdjiev et al., (2018), Transmission of Monetary Policy Through Global Banks: Whose Policies Matter, BIS Working Paper Nr. 737, August 20, via bis.​org

In many other countries that was the case as well. See for an overview: V. Mendoza, P. Carville and B. Larking, (2011), Bank Taxes: Variations on a Theme, Derivatives and Financial Instruments, Vol. 13, Issue 4, pp. 222–230.

Wet Bankenbelasting, Staatsblad (Official Gazette) 2012, p. 325.

Kamerstukken (Parliamentary Documents) II 2011–2012, 33121, Nr. 3, p. 2.

In practical terms it means that the regulator was of the opinion that the FIs had an enhanced accountability and that that justified an enhanced contribution toward reducing the government deficit caused by the massive lending needed on the part of the sovereign to materialize the bailout. The bank tax does not mathematically try to equate the exact cost of the bailout mechanism. The taxes raised in this respect will also not be isolated for the purposes of future issues in this sector but will flow to the general means of the sovereign and be used at its discretion.

Although the Dutch government indicated that this was not the idea. See: Kamerstukken (Parliamentary Documents) II 2009/10, 31980, Nr. 9, p. 10; Kamerstukken (Parliamentary Documents) II 2010/11, 21501-07, Nr. 791, p. 15.

The difference in rates is governed by the remaining term of the debts as at the balance sheet date, facilitating redemption and refinancing when long-term debt becomes current debt in its final year.

P. Kavelaars, (2012), Netherlands Bank Tax Introduced, European Taxation (August), paragraph 3.3., pp. 437–442. Van der Geld seems to agree, although not explicitly, see J. Van der Geld, (2013), Bankenbelasting: de foute oplossing voor een echt probleem, Rijkers Bundel, Prisma Print, pp. 115–120.

Kavelaars, (2012), Ibid. p. 441.

Which equals the rate(s) they charge to their customers and the cost of funding for the bank. In general accounting terms the gross interest margin for a bank (at least regarding their lending activities) equals the gross margin for other corporations.

With variations based on what is financed. The longer the term of the loans financed in the market the higher the interest rate. This is the direct consequence of the opportunity cost of capital principle, that is, capital that is locked away for a longer period of time will trigger a higher interest rate as that amount of capital cannot be put to work elsewhere for a long period of time, even if better risk-adjusted opportunities would occur in the market. That loss of agility comes at a higher cost.

MMFs are essentially open-ended mutual funds that only invest in short-term risk-free (or near risk-free) debt instruments. They are therefore known for their high level of liquidity. Investors tend to use these vehicles as a quasi-bank account in order to generate a slightly higher return. Access to the full principle amount of their investment at all times is a pre-condition.

And which has triggered new regulation on MMFs in both the US and Europe: see for Europe: Proposal for a Regulation of the European Parliament and of the Council on Money Market Funds, Com (2013) 615 final of September 9, 2013. Given their high level of liquidity these were the first vehicles to be impacted when the market shake-out started, and a fire sale was initiated. A ‘fire-sale’ can be defined as any sale that investors engage in, under distress, selling assets below their intrinsic value in order to provide liquidity to their customers or to meet proprietary liabilities.

The regulators turn the tables on the issue: The Dutch government stated that research has shown that short-term funding accelerated the spreading of liquidity problems in the banking sector in recent years (Kamerstukken (Parliamentary Documents) II, 2011/12, 33121, Nr. 4, p. 7).

It does not really differentiate how a bank is financed relative to others and the cost will most likely be pushed forward to the bank’s customers (see also Van der Geld, Ibid. p. 117).

C. M. Buch et al., (2014), Taxing Banks: An Evaluation of the German Bank Levy, Deutsche Bank Discussion Paper, Nr. 38/2014. ‘First, they decrease their total stock of loans whereas the provision of new loans remains unaffected. Second, banks try to attract deposits which are not subject to the tax by increasing deposit rates’ (Ibid. p. 32).

Com (2011) 594 final of September 28, 2011.

Com (2013)71 of February 2013, Proposal for a Council Directive implementing enhanced cooperation in the area of financial transaction tax.

At the time of closing the manuscript 10 countries (Austria, Belgium, France, Germany, Greece, Italy, Portugal, Slovakia, Slovenia and Spain) are still at the table. Expected implementation was foreseen by 2016 but extended to oversee the implications of Brexit in this matter. The legal basis was article 113 TFEU and Council Decision 2013/52/EU of January 22, 2013 authorizing enhanced cooperation in the area of financial transaction tax authorized the Member States listed in its Article 1 to establish enhanced cooperation in the area of FTT, O.J. L 22, 25.1.2013, p. 11. The EC, in May 2018, played down the initial 57 Billion USD in possible revenues to a meager 23.5Billion USD. It was also acknowledged that collecting could become a problem given the pending Brexit talks. Under the Austrian Presidency in the second half of 2018, the prospects deteriorated again as Austria indicated that the FTT proposal is simply a bad idea and that years of negotiations on the matter has created a situation where ‘the scope of the proposed levy has been scaled back so much that it isn’t worth the effort anymore’. See: B. Groendahl and A. Weber, (2018), Austria Says EU Financial Transaction Tax is on the Wrong Track, September 5, Bloomberg.​com. There is support building for that position from a variety of angles. See, for instance, M. Coelho, (2016), Dynamic and Cross-Platform Optimal Financial Transaction Tax, Working Paper, mimeo; E. Dávila, (2016), Optimal Financial Transaction Taxes, NYU Working Paper mimeo. In May 2019 both France and Germany tried to revive the FTT proposal, this time based on the (already existing) French financial transaction tax model.

Com(2013)71 of February 2013, Ibid. pp. 2&4.

See Chapter 1 of the proposal for details.

The Dutch Bureau for Economic Policy Analysis on December 21, 2011 in their note ‘[e]valuatie van de financiële transactiebelasting’ already, based on the 2011 FTT proposal, concluded that there is ‘little evidence that the introduction of a financial transaction tax within the EU will be effective in correcting market failures, and finds that other taxes are likely to be more efficient in raising revenues, involving lower deadweight losses’ (pp. 6–9). The Dutch Central Bank also finds the introduction of a financial transaction tax within the European Union undesirable. See DNBulletin (February 6, 2012).

See on the difficulty to measure the interconnectedness between the different players in the financial field and the lengthened financial (intermediation) chains: G. Houton and J.-C. Héan, (2014), How to Measure Interconnectedness between Banks, Insurers and Financial Conglomerates, Banque de France Working Paper, October, Mimeo.

Å. Johansson, et al., (2017). Tax Planning by Multinational Firms, OECD Economics Department Working Paper Nr. 1355, Paris.

D. Hirschleifer and S.H. Teoh, (2009), Systemic Risk, Coordination Failures and Preparedness Externalities, Journal of Financial Economic Policy, Vol. 1, Issue 2, pp. 128–142.

V. Acharya and P. Volpin, (2007), Corporate Governance Externalities, London Business School and Centre for Economic Policy Research (CEPR) Working Paper.

V.V. Acharya, H. Le and H. S. Shin, (2013), Bank Capital and Dividend Externalities, Princeton/NYU Working Paper Series.

Those risks that, when they occur, only impact the party/parties involved in the transaction.

Those risks that when they occur impact not only the engaged party/parties but also third parties.

See for a detailed analysis of systemic risk: O. de Bandt and Ph. Hartmann, (2011), What is Systemic Risk Today, ECB Working Paper, pp. 37–83 (original ECB Paper 2000), in particular pp. 40–48. See in general: O. de Bandt, Ph. Hartmann and J.L Peydró, Systemic Risk in Banking, The Oxford Handbook of Banking, in A. N. Berger, P. Molyneux, and. O. S. Wilson (eds.), Oxford University Press, Oxford; G.G. Kaufman and K.E. Scott, (2003), What is Systemic Risk and Do Bank Regulators Retard or Contribute to it, The Independent Review, Vol. VII, Issue 3, pp. 371–391.

Underlying contagion risk lies coordinated game theory (i.e. behaviors of market participants are (partly) determined by behaviors of other market participants). Or specifically in this case, withdrawals at one bank trigger withdrawals at another bank by increasing players’ beliefs that other depositors in their own bank will withdraw, making them more likely to withdraw as well. The level of contagion is higher the higher the FIs are interrelated; see in detail: M. Brown, S. Trautmann and R. Vlahu, (2014), Understanding Bank-Run Contagion, Working Paper Series Nr. 1711. See also for a novel approach to the contagion theory: T. Ahnert and C. Bertsch, (2015), A Wake-Up-Call Theory of Contagion, Bank of Canada/Banque de Canada Working Paper, Nr. 2015-14, April. The most problematic dimension of contagion risk is solvency contagion risk. This includes losses transmitted after banks default, but also losses due to the fact that creditors revalue their exposures when probabilities of default of their counterparties change. Solvency has been on the decline since the financial crisis mainly due to the increase in banks’ capital and the decrease in interbank exposures. See in detail: M. Bardoscia et al., (2017), The Decline of Solvency Contagion Risk, Bank of England Staff Working Paper Nr. 662, June.

It is fair to say that much of the literature has focused only on ‘direct’ contagion arising from firms’ contractual obligations. Direct contagion occurs if one firm’s default on its contractual obligations triggers distress (such as illiquidity or insolvency) at a counterparty firm. But contractual obligations are not the only means by which financial distress can spread, just as close human contact is not the only way that many infectious diseases are transmitted. Focusing only on direct contagion underestimates the risk of financial crisis given that other important channels exist. More recently attempts have been made to get a more holistic picture in place regarding indirect contagion and the channels which manifest even in the absence of direct contractual links. The first is the market price channel, in which scarce funding liquidity and low market liquidity reinforce each other, generating a vicious spiral. The second is information spillovers, in which bad news can adversely affect a broad range of financial firms and markets. See in detail including interesting literature list: L. Clerc et al., (2016), Indirect Contagion: The Policy Problem, ESBR Occasional Paper Series Nr. 9, January; R.Cont and E. Schaanning, (2017), Fire Sales, Indirect Contagion and Systemic Stress Testing, Norges Bank Working Paper Nr. 2. The latter conclude ‘even with optimistic estimates of market depth, moderately large macro-shocks may trigger fire sales which may then lead to substantial losses across bank portfolios, modifying the outcome of bank stress tests.’

After the bank run of 2008, Bernanke organized a monetary injection to fight deflation. When the probability of a bnk run is positive, depositors increase their money demand thereby reducing outstanding deposits. At the macro level that implies that the velocity of money drops and deflation arises. However, in some circumstances, monetary injections have no effect on prices but reduce money velocity and deposits. It has been argued that in case the Fed had not interfered in 2008, the MMF run would have been much smaller. See: R. Robatto, (2017), Flight to Liquidity and Systemic Bank Runs, ESRB Working Paper Nr. 38, March.

Credit rationing occurs different for different borrowers: publicly listed borrowers are rationed more by prices or interest rates (increase in interest rate spread), whereas privately held borrowers are rationed more by the number of loans, but minimal increases in interest rate spreads. Public borrowers are rationed more by price, whereas private borrowers are rationed more by quantities. Private and public firms are statistically not treated different unless one takes into account the granular data on borrower listing status. See in detail: A.N. Berger et al., (2018), Who Pays for Financial Crises? Price and Quantity Rationing of Different Borrowers by Domestic and Foreign Banks, IMF Working Paper Nr. WP/18/158, July. They also observe a re-pricing of borrower risk: interest rate spreads become significantly more sensitive to borrower leverage and credit rating during the crisis. A difference was also observed between foreign and domestic banks: foreign banks decreased lending quantities more and, among private borrowers, increased spread less than domestic banks. There are quite some policy implications reviewed (p. 22), not all of them which I agree with however (or at least not the policy design implication recommended implicitly or explicitly). See also: A.N. Berger et al., (2016), Reexamining the Empirical Relation between Loan Risk and Collateral: The Role of the Economic Characteristics of Collateral, Journal of Financial Intermediation, Vol. 26, pp. 28–46; P. Bolton et al., (2016), Relationship and transaction lending in a crisis. Review of Financial Studies, Vol. 29, pp. 2643–2676; S. Carbó-Valverde et al., (2016), Trade credit, the Financial Crisis, and Firm Access to Finance, Journal of Money, Credit, and Banking, Vol. 48, Issue 1, pp. 113–143; D. Weinstein, and M. Amiti, (2018), How Much do Idiosyncratic Bank Shocks Affect Investment? Evidence from Matched Bank-Firm Loan Data, Journal of Political Economy, Vol. 126, pp. 525–558.

See for an analysis of the (German) interbank intermediation market and their determinants: M. Bluhm et al., (2016), Interbank Intermediation, Deutsche Bundesbank Discussion Paper Nr. 16, Frankfurt am Main. Key conclusions are: (1) interbank lending accounts for a significant share of an average bank’s balance sheet, (2), the network of interbank loans is both sparse and persistent over time, (3), the average interbank exposure among commercial banks in Germany is longer than one year, and (4) banks hold significant interbank exposures on both sides of their balance sheet simultaneously. Banks use different maturity segments of the interbank market to manage their duration gap, defined as the difference between weighted maturity of a bank’s assets and liabilities. Also: N. Bulusu and P. Guérin, (2018), What Drives Interbank Loans? Evidence from Canada, Bank of Canada Staff Working Paper Nr. 5, January. Their results suggest that the key friction-driving behavior in this market is the collateral reallocation cost faced by borrowers. Borrowers therefore adjust unsecured lending in response to changes in short-term cash needs and use repos to finance persistent liquidity demand. They also find that lenders set rates and haircuts taking into account counterparty credit risk and collateral market price volatility. See for a US analysis after the large-scale asset purchases and the new Basel III regulation. It has made the US interbank market basically disappear. They conclude that the new regulations may engender changes in market structure that result in interbank trading being completely replaced by non-bank lending to banks when excess reserves become scarce. In detail: K. Kim et al., (2018), Can the US Interbank Market be Revived?, Finance and Economics Discussion Series Nr. 2018-088. Washington: Board of Governors of the Federal Reserve System, https://​doi.​org/​10.​17016/​FEDS.​2018.​088. Also in a broader context: F. de Fiore et al., (2019), Money Markets, Collateral and Monetary Policy, ECB Working Paper Nr. 2239, February 13. The latter paper seeks to understand the implications of these developments (miserable state of the interbank markets) for the broader economy and monetary policy.

S. Titman, (2013), Financial Markets and Investment Externalities, The Journal of Finance, Vol. 68, Issue 4, pp. 1307–1329.

For example caused by a fire sale (financial accelerator) of collateral assets (pecuniary externality) undervaluing liquidity. See M. Miller and L. Zhang, (2011), Whither Capitalism. Financial Externalities and Crisis, Warwick Working Paper on coordination problems (information-based runs, etc.). On the economy-wide effects of the collateral channels see: G. Cerqueiro et al., (2016), Collateral damage? On Collateral, Corporate Financing and Performance, ECB Working Paper Series Nr. 1918, June; G. Cerqueiro et al., (2019), Collateral Damaged? Priority Structure, Credit Supply, and Firm Performance, Norges Bank Working Paper Nr. 9, May 29. The latter analysis is based on a unique legal reform in 2004 in Sweden redistributed collateral rights from banks holding floating liens to unsecured creditors without changing the value of assets on firms’ balance sheets.

In a financial network, the concept of financial centrality plays a role. Financial centrality of an agent in a financial network can be defined as the ex ante marginal social value of injecting an infinitesimal amount of liquidity to that agent. See in extenso: A. Chandrasekhar et al., (2018), Financial Centrality and the Value of Key Players, Working paper, mimeo.

P. Glasserman and H.P. Young, (2013), How Likely is Contagion in Financial Networks, Columbia Business School Working Paper, mimeo; reproduced as Office of Financial Research, Working Paper Nr. 9 of June 21 and later on updated in Journal of Economic Literature (2017), Vol. 54, Issue 3, pp. 779–831.

See, for instance, M. Brunnermeier et al., (2013), Assessing Contagion Risks from the CDS Market, ESBR Occasional Paper Series Nr. 4, September.

G. Creamer, (2017), Network Structure and Systemic Risk in the European Equity Market, IEEE Systems Journal, Vol. 99, pp. 1–9; K. Anand et al., (2018), The Missing Links: A global study on uncovering financial network structure from partial data, Journal of Financial Stability. Vol. 35, pp. 107–119.

Glasserman and Young, (2015), Ibid. pp. 27–28. See also: D. Acemoglu, et al., (2013), Systemic Risk and Stability in Financial Networks. Working Paper Nr. 18727, NBER, Cambridge, Massachusetts and R. Bookstaber et al., (2015), Process Systems Engineering as a Modeling Paradigm for Analyzing Systemic Risk in Financial Networks, Office of Financial Networks, OFR Working Paper Nr. 15-01, February 11. Bookstaber et al. highlight that as financial instability often results from positive feedback loops intrinsic to the operation of the financial system, the challenging task of identifying, modeling and analyzing the causes and effects of such feedback loops requires a proper systems engineering perspective, which is lacking in the remedies proposed in recent literature. They propose a ‘signed directed graphs’ model that is able to represent and reveal information missed by more traditional network models of financial system. This framework adds crucial information to a network model about the direction of influence and control between nodes, providing a tool for analyzing the potential hazards and instabilities in the system. See also: F. Caccioli et al., (2017), Network Models of Financial Systemic Risk: A Review, Working Pper, mimeo.

Systemic risk is the danger of economy-wide financial feedback effects whereby adverse economic shocks force market participants to sell assets in order to raise liquidity, and the sales in turn push down asset prices and force them to sell even more of their asset holdings. On the productive side of the economy, this is mirrored in declines in output. Regarding the impact of the intrinsic value of macroeconomic news on asset prices see: T. Gilbert et al., (2016), Is the Intrinsic Value of Macro-Economic news announcements Related to their Asset Price Impact, ECB Working paper Series Nr. 1882, February. See for a broader analysis of the relationship between asset prices and monetary policy: L. Alessi and M. Kersenfischer, (2016), The Response of Asset Prices to Monetary Policy Shocks: Stronger then Thought, ECB Working Paper Serie Nr. 1967, September. Key finding is that, using a structural factor model (SFM) (which has material benefits compared to a vector autoregression model [VAR]), particularly in a low interest rate environment the impact is material. They conclude that ‘asset prices respond more and more quickly to monetary policy shocks than commonly thought’…and that ‘monetary policy is much more important in explaining asset price movements compared to available evidence’ (pp. 26 ff).

See for an analysis in the housing sector: J. Bianchi and E. Mendoza, (2011), Overborrowing, Financial Crises and ‘Macro-Prudential’ Policy, IMF Working Paper Nr. WP/11/24. They conclude that ‘[a]gents in a decentralized competitive equilibrium do not internalize the negative effects of asset fire-sales on the value of other agents’ assets and hence they borrow too much ex ante, compared with a constrained social planner who internalizes these effects. [The] average debt and leverage ratios are slightly larger in the competitive equilibrium, but the incidence and magnitude of financial crises are much larger. Excess asset returns, Sharpe ratios and the market price of risk are also much larger’.

A. Korinek, (2011), Systemic Risk Taking: Amplification Effects, Externalities and Regulatory Responses, ECB Working Paper Series, Nr. 1345. He asserts: ‘[e]ven though they (individual market participants (ed.)) may have access to a complete market to insure against systemic risk, they insure to a socially inefficient extent because when they trade off the costs and benefits of insurance, they do not internalize the social benefits of insurance in the form of mitigating the economy-wide fire sales. By contrast, a policymaker has the capacity to internalize this externality and make everybody better off by inducing financial market participants to reduce their systemic risk-taking. This in turn will lead to lower fire sales, smaller price declines and greater macroeconomic stability’(p.5). Also: C. Gilbert et al., (2018), Monetary Policy and Long-Run Systemic Risk taking, Banque de France Working Paper Nr. 694, September 14.

The theory development of multilayer networks is still in its infancy, and its further development looks more like meandering than straightforward theory development. Understanding complex systems, including multilayer interconnected financial markets, is a route that involves many choices. Sergueiva et al. argue ‘[t]he empirical studies have considered the structure as a non-interconnected multiplex rather than as an interconnected multiplex network. No mechanism of multichannel contagion has been modeled and empirically evaluated, and no multichannel stabilisation strategies for preemptive contagion containment have been designed.’ A. Sergueiva et al., (2017), Journal of Quantitative Finance, Vol. 17, Issue 12, pp. 1885–1904. They highlight that existing studies that use multilayer networks, in their multiplex form, to analyze the structure of financial systems, have (1) considered the structure as a non-interconnected multiplex network, (2) no mechanism of multichannel contagion has been modeled and empirically evaluated and (3) no multichannel stabilization strategies for pre-emptive contagion containment have been designed. Their model formulates an interconnected multiplex structure, and a contagion mechanism among financial institutions due to bilateral exposures arising from institutions’ activity within different interconnected markets that compose the overall financial market. The empirical simulations their analysis produces confirm their capability for containing contagion. The potential for multichannel contagion through the multiplex contributes more to systemic fragility than single-channel contagion; however, multichannel stabilization also contributes more to systemic resilience than single-channel stabilization.

T. A. Peltonen et al., (2015), Interconnectedness of the Banking Sector as a Vulnerability to Crises, ECB Working Paper Nr. 1866; a more central position of the banking sector in the macro-network increases the probability of a banking crisis. Regarding the aspect of uncertainty in the context of financial interconnectedness: C. Bertrand et al., (2018), Global Financial Interconnectedness: A Non-Linear Assessment of the Uncertainty Channel, Banque de France Nr. 661, January 24. Results clearly show that high uncertainty tends to generate more connectedness. From an economic policy point of view, this result suggests that in the presence of high uncertainty, an adverse financial shock in a specific country is likely to propagate more widely and more strongly to the whole financial system.

See in detail: P. Glasserman, (2015), Contagion in Financial Networks, Office for Financial Research Working Paper, 15–21, October 20, in particular pp. 66–67. See also: T. Wag, (2017), Financing through Money Creation, Too Connected to Fail and Systemic Risk, University of Essex Working Paper, mimeo; H. Mao, (2017), Systemic Risk and Cyclical Eco; shadow bank money creation significantly expands during monetary tightening. This ‘shadow money channel’ offsets the reductions in commercial bank deposits and dampens the impact of monetary policy. Nomic Environment, Shanghai Second Polytechnic University Working Paper, October 24, mimeo. Xiao finds that explains the difference in monetary transmission between commercial and shadow banks. Facing more yield-sensitive clientele, shadow banks pass through more rate hikes to depositors, thereby attracting more deposits when the Fed raises rates. That would imply that monetary tightening unintentionally drives deposits into the uninsured shadow banking sector, amplifying the risk of bank runs. In detail: K. Xiao, (2019), Money Transmission through Shadow Banks, Columbia University Working Paper, April 20, mimeo.

See for a novel approach based on basis swap spreads: J. Lafuente et al., (2016), Dissecting Interbank Risk, Working Paper, mimeo, October 5: they ‘analyse interbank risk using the information content of basis swap (BS) spreads, floating-to-floating interest rate swaps whose payments are associated with euro deposit rates for alternative tenors.’ They demonstrate that ‘changes in systemic risk are associated with regime shifts. Shocks to aggregate liquidity are also important for describing persistent changes in BS spreads. Sovereign risk and risk aversion are further relevant factors explaining transitory fluctuations.’

The interconnectedness can be mostly explained by investors’ search-for-yield behaviour, financial linkages between banks, capital stringency and demand from institutional investors. Interconnectedness doesn’t recognize national boundaries. See: T. Fong et al., (2018), Assessing the Interconnectedness between Cross-Border Shadow Banking Systems, HKIMR Working Paper Nr. 5, July.

See also: E, Grant, (2016), Exposure to International Crises: Trade vs. Financial Contagion, Federal Reserve Bank of Dallas, Globalization and Monetary Policy Institute, Working Paper Nr. 280, August; G. Cappelletti and P.E. Mistrulli, (2017), Multiple Lending, Credit Lines and Financial Contagion, ECB Working Paper Nr. 2089, July. The latter examine a long time ignored channel of contagion, for instance, the existence of multiple credit lines. Multiple credit lines exist for a variety of reasons including the diversification of credit risk by lenders, borrowers diversify maturity and premature liquidation risk and of course to avoid or mitigate hold-up problems. They also conclude that during a crisis, multiple lending may be an important channel for contagion. They comment ‘[w]hen the typical markets for liquidity are impaired, banks may call back credit lines in order to obtain cash from borrowers. However, a reasonable reaction of the latter is that of drawing money from credit lines available at other banks thus propagating the liquidity shocks within the banking system. We find that this channel of contagion might have a significant impact on the stability of the banking system, in particular when that channel interacts with other channels for contagion related to direct interbank exposures’ (p. 25). Multiple credit channels propagate and amplify liquidity shocks during a crisis.

During late 2017 the Systemic Risk and Interconnectedness (SyRIN) tool was presented. It allows a comprehensive assessment of systemic risk via quantification of the impact of risk amplification mechanisms, due to interconnectedness structures across banks and other financial intermediaries—insurance, pension fund, hedge fund and investment fund sectors, which cannot be captured when analyzing sectors independently. It produces a variety of outcomes to evaluate systemic risk and this from complementary perspectives: tail risk, cross-entity interconnectedness and the contribution to systemic risk by different entities and sectors. See for details: F. Cortes et al., (2018), A Comprehensive Multi-Sector Tool For Analysis of Systemic Risk and Interconnectedness (SyRIN), IMF Working paper Nr. WP/18/14, December (2017).

J.M. Londono, (2016), Bad Contagion, Board of Governors of the Federal Reserve System, International Finance Discussion Papers Nr. 1178, September.

See for these models: K.H. Bae, et al., (2003), A New Approach to Measuring Financial Contagion, Review of Financial Studies, Vol. 16, pp. 717–763; G. Bekaert, et al., (2014), The Global Crisis and Equity Market Contagion, Journal of Finance, Vol. 69, pp. 2597–2649; G. Bekaert, (2005), Market Integration and Contagion, Journal of Business, Vol. 78, pp. 39–69; G. Bekaert, (2013), Risk, Uncertainty and Monetary Policy, Journal of Monetary Economics, Vol. 60, pp. 771–788; C.T.Brownlees, and R.F. Engle, (2017), SRISK: A Conditional Capital Shortfall Measure of Systemic Risk, ESRB Working Paper Nr. 37, March. (also published as Review of Financial Studies, Vol.30, Nr.1, pp. 48–79). Also: J. Bian et al., (2017), Leverage Networks and Market Contagion, Working Paper, November, mimeo; E. Gerba and D. Żochowski, (2017), Knightian Uncertainty and Credit Cycles, ECB Working Paper Nr. 2068, May.

R. Heath and E.B. Goksu, (2017), Financial Stability Analysis: What are the Data Needs, IMF Working paper Nr. WP/17/153, July. Weaks spots identified are ‘shadow banking, capital flows, corporate borrowing, and granular data’.

R. Rigobon, (2016), Contagion, Spillover and Interdependence, ECB Working Paper Series Nr. 1975, November. He concludes ‘[i]n consequence, correlations, principal components, OLS regressions, event studies, VAR’s, Arch and Garch models, Probit and Logit, are all biased and time dependent. This is not because the structural parameters of the data generating process are unstable but because the models are all misspecified.’… ‘The problem is even more complicated because there are no natural experiments nor instruments that could solve the identification problem (p. 19).’

Y. Korniyenko et al., (2019), Evolution of the Global Financial Network and Contagion: A New Approach, IMF Working Paper Nr. WP/19/113, May. Also: E.M. Cerutti and H. Zhou, (2017), The Global Banking Network in the Aftermath of the Crisis: Is there Evidence of De-Globalization? IMF Working Paper, Nr. WP/17/232; P.R. Lane et al., (2017), International Financial Integration in the Aftermath of the Global Financial Crisis, IMF Working Paper Nr. WP/17/115, May.

For the relationship between bank characteristics and financial contagion, see: S. Mazumder and L.R. Piccotti, (2019), Financial Contagion and Bank Characteristics, Working Paper, May 25, mimeo. While tier 1 capital requirements and greater financial constraints reduce a bank’s contagion profile, the effect is non-monotonic. Banks with relatively higher total capital ratios tend to contribute more to financial contagion. Geographic distance between banks is negatively related to contagion profiles and they find evidence that institutional ownership increases banks’ contagion profiles.

P. Fève et al., (2019), Shadow Banking and the Great Recession: Evidence from an Estimated DSGE Model, Banque Central de Luxembourg Working Paper Nr. 125, March. Also: P. Fève and O. Pierrard, (2017), Financial Regulation and Shadow Banking, Banque Central de Luxembourg Working Paper Nr. 111, July. Shadow banking interferes with macroprudential policies. More precisely, asymmetric regulation causes a leak toward shadow banking, which weakens the expected stabilizing effect, they conclude.

Glasserman, (2015), Ibid. p. 67. To date there has been relatively little research on how institutions actually behave when determining how to ration short-term funding in times of financial stress. See, for instance, K. Fink et al., (2015), The Credit Quality Channel: Modeling Contagion in the Interbank Market, Deutsche Bundesbank Discussion Paper, Nr. 38/2015. Also: M. Pritsker, (2017), Choosing Stress Scenarios for Systemic Risk Through Dimension Reduction FRB Boston Risk and Policy Analysis Unit Paper Nr. RPA 17-4.

D. Wang et al., (2018), Do Information Contagion and Business Model Similarities Explain bank Credit Risk Commonalities, DNB Working Paper Nr. 619, December 20.

T. Ahnert and C.-P. Georg, (2017), Information Contagion and Systemic Risk, Bank of Canada Working Paper Nr. 2017-29. Also: K. Anand et al., (2016), Capturing Information Contagion in a Stress-Testing Framework, Deutsche Bundesbank Discussion Paper Nr. 29, Frankfurt am Main. They define information contagion as ‘whereby bad news about one institution precipitates a loss of confidence in the security of holdings across the banking system’. A major problem until today is the quantification of information contagion. Anand et al. developed a model-based stress-testing framework where the solvency risks, funding liquidity risks and market risks of banks are intertwined. The key transmission mechanism is a two-way interaction between the beliefs of secondary market investors and the coordination failure between the creditors of financial institutions. Their findings are material: Not only can information contagion be significant (the probability of one bank entering distress due to contagion alone is nearly 20%, and the extreme tail associated with the system-wide distribution of bank losses is materially larger. A particular interesting feature is the fact that they decompose aggregate losses to the banking system according to their origin, that is, solvency risk, funding liquidity risk and information contagion. See also more recently: T. Ahnert and Co-Pierre Georg, (2017), Information Contagion and Systemic Risk, Bank of Canada Staff Working Paper Nr. 2017-29, July. They examine the effect of ex post information contagion on the ex ante level of systemic risk defined as the probability of joint bank default. They conclude that ‘[w]hen banks are subject to common exposures, information contagion induces small adjustments to bank portfolios and therefore increases overall systemic risk. When banks are subject to counterparty risk, by contrast, information contagion induces a large shift toward more prudential portfolios, thereby reducing systemic risk.’ Further: P. Cerchiello and G. Nicola, (2018), Assessing News Contagion in Finance, Econometrics, Vol. 1, Issue 5, pp. 1–19; T. Ahnert and C. Bertsch, (2018), A Wake-Up Call Theory of Contagion, working Paper, March, mimeo. Key finding here, based on global coordination games, is that contagion can even occur after investors learn that regions are unrelated (zero macro shock); T. Ahnert, and A. Kakhbod, (2017), Information Choice and Amplification of Financial Crises, Review of Financial Studies, Vol. 30, Issue 6, June, pp. 2130–2178; Z. Li and K. Ma, (2017), A Theory of Endogeneous Asset Fire Sales, Bank Runs and Financial Contagion, May, Working Paper, mimeo; J. Idier and T. Piquard, (20170, Pandemic Crises in Financial Systems: A Simulation-Model to Complement Stress-Testing Frameworks, Banque de France Working Paper Nr. 621, January.

‘One bank’s investors find information about another bank’s solvency valuable for two reasons. First, both banks might have invested into the same asset class like risky sovereign debt or mortgage backed securities. Learning about another bank’s profitability helps the investor assess the profitability of its bank. Second, one bank might have lent to the other, for instance as part of a liquidity risk-sharing agreement. Learning about the debtor bank’s profitability helps investors assess the counterparty risk of the creditor bank’ (p. 24).

See for the ability to predict bank defaults using systemic risk measures: Arndt-Gerrit Kund, (2018), Systemic Risk and Bank Defaults: Assessing the Explanatory Power of Systemic Risk Measures in Forecasting Bank Defaults, University of Cologne Working Paper, January 10, mimeo. Using systemic risk models in the ‘probability of default model’ might not always improve the initial model; it does entail a substantial improvement of correct predictions. Also: P. Smaga et al., (2016), Can Banks Default Overnight? Modeling Endogenous Contagion on O/N Interbank Market, Working Paper, mimeo. They show how the intrasystem cash fluctuations (the interbanking loan market), ‘without any external shocks, may lead to systemic defaults, what may be a symptom of the self-organized criticality of the system’. See regarding predicting financial stress: T. Duprey and B. Klaus, (2017), How to Predict Financial Stress? An Assessment of Markov Switching Models, ECB Working Paper Nr. 2057, May. They conclude that ‘the debt service ratio, the property price-to-rent ratio and the annual property price growth significantly affect the probability of entering a high financial stress regime, whereas the credit-to-GDP gap and the economic confidence indicator contribute significantly to the likelihood of exiting a high financial stress episode.’ (p. 21). See regarding the role of contagion in the transmission of financial stress: M.C. Herculano, (2018), The Role of Contagion in the Transmission of Financial Stress, ESRN Working Paper Nr. 81, August 2. He identifies that bank default likelihoods depend, to a large extent, on peer effects that account on average for approximately 35% of total distress. Furthermore, he finds evidence of significant heterogeneity amongst banks and some institutions to be systemically more important that others, in terms of the peer effects.

Regarding the mitigation of counterparty risks: Y. Gündüz, (2018), Mitigating Counterparty Risk, Deutsche Bundesbank Discussion Paper Nr. 35, September 14.

See G. Cappelletti and G. Guazzarotti, (2017), The Role of Counterparty Risk and Assymetric Information in the Interbank Market, ECB Working Paper Nr. 2022, February. The rise in counterparty risk substantially decreases the probability of obtaining funds from foreign banks. The effect in the interbank market is meaningful, in particular in the unsecured market, because it is more information insensitive.

See for a very interesting country-specific (i.e. Switzerland) paper regarding systemic risk and contagion analysis: IMF, (2014), TN- Systemic Risk and Contagion Analysis, September. See also: V.V. Acharya and T. Yorulmazer, (2002), Information Contagion and Inter-Bank Correlation in a Theory of Systemic Risk, Working Paper, December 21; V.V. Acharya and T. Yorulmazer, (2006), Information Contagion and Bank Herding, Working Paper, July, mimeo: information contagion induces profit-maximization of bank agents with other bank agents. Herding was also identified across pension funds, that is, they change their strategic allocations in the same direction over time. R. Bauer et al., (2018), Pension Fund Interconnectedness and Hrd Behavior, DNB Working Paper Nr. 612, October 30 and I. Koetsiers and J. Bikker, (2018), Herding Behavior of Dutch Pension Funds in Asset Class Investments, DNB Working Paper Nr. 602, July 24. See regarding the relation between bank herding and systemic risk: Y. Heo, (2019), The Impact of Bank Herding on Systemic Risk, Working Paper, April 4, mimeo.

See also: C.-P. Georg, (2013), Information Contagion and Systemic Risk, Marie Curie ITN Presentation, Konstanz, April 12.

Although regulating and taxing do not constitute the same dynamics, they do remind us of the stand-off occurring in the FI sector between fiscal instruments and command-and-control legislation all trying to achieve the same objective, but in different ways, the stability of the financial markets and the mitigating of risk exposures.

See in extenso: D. Masciandaro and F. Passarelli, (2013), Financial Systemic Risk: Taxation or Regulation, Journal of Banking and Finance, Vol. 37, Issue 2, pp. 587–596.

Masciandaro and Passarelli, Ibid. pp. 594–596.

The traditional Command-and-Control Legislation as embodied by the CRD packages lately updated in 2014 (CRD IV) and CRD V/CRR II (2019). Although there is initial consensus that capital requirements did improve financial stability, it also became clear that standardized implementation of capital requirements globally took place without taking into account the local dynamics of the financial sector. But to build an effective prudential framework, they may need to adapt international standards taking into account the sophistication and size of their financial institutions, the relevance of different financial operations in their market, the granularity of information available and the capacity of their supervisors. Under a proportionate application of the Basel standards, smaller institutions with less complex business models would be subject to a simpler regulatory framework that enhances the resilience of the financial sector without generating disproportionate compliance costs. Ferreira et al. provide guidance on the matter. See in detail: C. Ferreira et al., (2019), From Basel I to Basel III: Sequencing Implementation in Developing Economies, IMF Working Paper Nr. WP/19/127, June.

Through a thorough revamp (proposal of July 12, 2010, recast, Com/2010/0368 final) of the Deposit Guarantee Scheme Directive: Directive 94/19/EC of the European Parliament and of the Council of May 30, 1994 on deposit-guarantee schemes, O.J. L 135, 31/05/1994, pp. 5–14. The proposal was approved by the European parliament on April 15, 2014 and published in June 2014: Directive 2014/49/EU of the European Parliament and of the Council of April 16, 2014 on deposit guarantee schemes, O.J. L 173, pp. 149–178.

Despite many challenges and adverse implications. See: M. Dell’Era, (2018), Financial Transaction Taxes and Expert Advice, NBS Working paper Nr. 2018/4, October.

In its most extensive form, a financial activities tax is assessed on total profit and wages, and can be viewed as a tax on a proxy for total value added by a financial sector company. The European Commission views a financial activities tax as a potential solution to the current VAT exemption of financial services, which the commission feels provides (undesired) benefits to the financial sector. See Com(2010)549 final, Communication regarding taxation of the financial sector of October 7, 2010). A proposal for a financial activities tax has not been released, most likely because it has focused on the introduction of the FTT and because it requires an assessment of the interaction with the VAT system if introduced. See also: V. van der Lans, (2012), The Proposed Bank Tax: To Tax or Not To Tax, Derivatives and Financial Instruments, IBFD, pp. 52–62.

Corporate tax systems work in principle according to an origin-based principle. In more recent times however the idea of a destination-based model (like consumption taxes) has risen to prominence. Profits would be taxed where the sales take place. See: S. Hebous and A. Klemm, (2018), A Destination-Based Allowance for Corporate Equity, IMF Working Paper Nr. WP/18/239, November. In the context of shadow banking, corporate tax and interest-based deductions, the concept is interesting as it can facilitate an allowance for corporate equity or corporate capital. The benefit is that it neutralized the different treatment of capital sources as is the case in the current corporate tax model (pp. 8–11). To avoid accounting tricks, not only a destination-based model is suggested but also a shift from corporate taxation on accounting net income to a cash-flow-based tax model has been recommended in recent years. That would boost investments and economic output. See, for instance, B. Carton et al., (2019), Corporate Tax Reform: From Income to Cash Flow Taxes, IMF Working Paper Nr. WP/19/13, January. Although I have serious issues with some of the statements made in the report, this is not the time and place to detail them. My issues relate mainly to the fact that cash-flow-based models on a domestic and thus on a territorial-based frame are very vulnerable to avoidance. Further, the idea that it boosts economic output is very doubtful and the evidence unconvincing even when not taking into account possible avoidance. Spillovers that are positive in the long run are spillovers that often never show up. The authors also implicitly demonstrate the material weakness when indicating that the reform would work best when ‘when all countries undertake the reform’. We all appreciate how difficult that is. But there is good news: the main benefit of such a model is that interest payments would no longer be tax deductible. Having said that: taxing free cash flows implies interest deductibility, so a new definition of taxable cash flows would have to be defined. See further regarding the model: A. J. Auerbach, (2017), Destination-Based Cash Flow Taxation, Working Paper Nr. 17/01, Oxford University Centre for Business Taxation, Oxford (also as NBER Working paper Nr. 23881); S. Hebous et al., (2019), Revenue Implications of Destination-Based Cash-Flow Taxation, IMF Working Paper Nr. WP/19/07, January. The latter also point to the fact that large and globally integrated countries would be a material loser of taxable objects when transitioning from profit to cash-flow taxation.

At least technically in its purest form.

À la Capital Requirement Directive (CRD).

A similar conclusion was reached by M. Keen, (2011), The Taxation and Regulation of Banks, IMF Working Paper, WP 11/206. He concludes: ‘[t]he results suggest a potential role for taxing bank borrowing, perhaps as an adjunct to minimum capital requirements, at marginal rates that rise quite sharply at low capital ratios, reaching levels higher than those of the bank taxes so far adopted or proposed’(p. 30). He observed externalities in two different fashions: those that arise when such institutions are simply allowed to collapse, and those that arise when, to avoid the harm this would cause, their creditors are bailed out. He also asks whether corrective taxation or a regulatory capital requirement is the better way to address these concerns (above); See also M. Keen, (2011), Rethinking the Taxation of the Financial Sector, CESifo Economic Studies, Vol. 57, Issue 1, pp. 1–24.

The High-Level Group on Financial Supervision in the EU, (2009), chaired by J. de Larosière.

High-level Expert Group on Reforming the Structure of the EU Banking Sector, (2012) chaired by E. Liikanen.

See also S. Titman, (2013), Financial markets and Investment Externalities, The Journal of Finance, Vol. 68, Issue 4, pp. 1307–1329.

G.S. Eskeland, (1994), A Presumptive Pigovian Tax Complementing Regulation to Mimic an Emission Fee, World Bank Economic Review, Vol. 8, Issue 3, pp. 373–394.

J.E. Stiglitz, (1989), Using Tax Policy to Curb Speculative Short-term Trading, Journal of Financial Services Research, Vol. 3, pp. 101–115.

A.R. Admati, P.M. DeMarzo, M.F. Hellwig and P. Pfleiderer, (2010), Fallacies, Irrelevant Facts, and Myths in the Discussion of Capital Regulation: Why Bank Equity is Not Expensive, Stanford Graduate School of Business Research Paper Nr. 2065.

V. Acharya, T. Phillippon, M. Richardson and N. Roubini, (2009), Financial Markets, Institutions and Instruments, Vol. 18, Issue 2, pp. 89–137.

K.R. French et al. (2010), The Squam Lake Report: Fixing the Financial System, Princeton University Press, Princeton.

D. Masciandaro and F. Passarelli, (2013), Financial Systemic Risk: Taxation or Regulation, Journal of Banking and Finance, Vol. 37, Issue, 2, pp. 586–596; L. Kaplow and S. Shavell, (2002), On the Superiority of Corrective Taxes to Quantity Regulation, American Law and Economics Review, Vol. 4, Issue 1, pp. 1–17.

The revenue-based models will not be extensively analyzed in the main analysis of this chapter which will focus on externality-neutralizing instruments. Revenue-based models will however be subject to a limited analysis in 11.5.2. regarding alternative models of taxation for FIs. See for extensive coverage of the different models designed and suggested in recent years: M. Keen, (2011), The Taxation and Regulation of Banks, IMF Working paper Series, WP/11/206 and M. Keen and R.A. de Mooij, (2012), Debt, Taxes and Banks, IMF Working Paper Series, WP/12/48; S. Claessens et al. G. Dell’ Arriccia, D. Agiz and L. Laeven, (2010), Lessons and Policy Implications from the Global Financial Crisis, IMF Working Paper Series, WP/10/44;R.A. de Mooij and M.P. Devereux, (2011), An Applied Analysis of ACE and CBIT Reforms in the EU, International Tax and Public Finance, Springer, Vol. 18, Issue 1, pp. 93–120; M.P. Devereux, (2012), Issues in the Design of Taxes on Corporate Profit, National Tax Journal, National Tax Association, Vol. 65, Issue 3, pp. 709–730; R.A. de Mooij, (2011), Tax Biases to Debt Finance: Assessing the Problem, Finding Solutions, IMF Staff Discussion Note, SDN11/11 and (2012) in Fiscal Studies, Vol. 33, Issue 4, pp. 489–512; R.A. de Mooij, M. Keen and M. Orihara, (2013), Taxation, Bank Leverage and Financial Crises, IMF Working Paper Series, WP/13/48;J. Vella, C. Fuest and T. Schmidt-Eisenlohr, (2011), The EU Commission’s Proposal for a Financial Transaction Tax British Tax Review, Vol. 6, pp. 607–621; J. English, J. Vella and A. Yevgenyeva, (2013), The Financial Tax Proposal Under the Enhanced Cooperation Procedure: Legal and Practical Considerations, British Tax Review, Vol. 2, pp. 223–259.

See in detail: M.P. Devereux, N. Johannesen and J. Vella, (2013), Can Taxes Tame the Banks? Evidence from European Bank Levies, Working Papers Nr. 1325, Oxford University Centre for Business Taxation.

Taxing bank bonuses, bank levies on bank assets over a certain threshold and so on.

See also: B. Coulter, C. Mayor and J. Vickers, (2012), Taxation and Regulation of Banks to Manage Systemic Risk, Oxford University Presentation.

P. Benczur et al., (2017), Evaluating the effectiveness of the new EU bank regulatory framework: A farewell to bail-out?, Journal of Financial Stability, Vol. 33, December, pp. 207–223.

Contagion risk can be broken down in three different ‘distance to risk measures’: distance to default, distance to capital and distance to inefficiency. See: K. Daly et al., (2017), Contagion Risk in Global Banking Sector, Working Paper, March 23, mimeo. The distance to default is measured by the distance of a theoretical default condition and current condition of a financial institution (p. 5). Other distance criteria are measures alongside this model based on Meeton’s option pricing technique.

G. Cannas et al., (2014), Financial Activities Taxes, Bank Levies and Systemic Risk, EC Taxation Papers Series, Working Paper Nr. 43, who review the different levies introduced.

See for the quantification of spillover effects: F. Vitek, (2017), Policy, Risk and Spillover Analysis in the World Economy: A Panel Dynamic Stochastic General Equilibrium Approach, IMF Working Paper Nr. WP/17/89, April. This panel dynamic stochastic general equilibrium model features a range of nominal and real rigidities, extensive macrofinancial linkages, and diverse spillover transmission channels. These macrofinancial linkages encompass bank and capital market based financial intermediation, with financial accelerator mechanisms linked to the values of the housing and physical capital stocks.

Leaders’ Statement: The Pittsburgh Summit, 2009, Strengthening the International Financial Regulatory System.

See for more recent credit booms and their dynamic: C. Hansman et al., (2018), Riding the Credit Boom, NBER Working Paper Nr. 24586, May.

Underlying those phenomena was obviously the FED’s interest rate policy as an undercurrent determining the course of events.

T. Hemmelgarn and G. Nicodème, (2010), The 2008 Financial Crisis and Taxation Policy, Working Paper Nr. 20-2010. European Commission, Luxembourg; J. Slemrod, (2009), Lessons for Tax Policy in the Great Recession. National Tax Journal, Vol. 62, Issue 3, pp. 387–397.

D.N. Shaviro, (2009), The 2008–09 Financial Crisis: Implications for Income Tax Reform, New York University Law and Economics Working Paper Nr. 09-35. New York University, New York, NY.

F. Longstaff and I. Strabulaev, (2014), Corporate Taxes and Capital Structure: A Long-Term Historical Perspective, NBER Working Paper, Nr. 20372. See regarding the impact of foreign institutional ownership on corporate taxation and avoidance: I. Hasan et al., (2016), The Effect of Foreign Institutional Ownership of Corporate Tax Avoidance: International Evidence, Bank of Finland Discussion Paper Nr. 26, Helsinki. See for a good contemporary overview of all corporate tax avoidance models and schemes (including transfer mispricing, international debt shifting, treaty shopping, tax deferral and corporate inversions: S. Beer et al., (2018), International Corporate Tax Avoidance: A Review of the Channels, Effect Sizes, and Blind Spots, IMF Working Paper Nr. WP/18/168, July, including the extensive literature list for reference: pp. 31–40. Specifically, shadow banking instruments and in particular securitization instruments are used to reduce the effective tax rate of the engaging firms. Uhde documents that banks may reduce their tax expenses through securitization via a direct and indirect channel. The results suggest that securitization may be described as an appropriate instrument to pursue tax avoidance, while the tax expense-reducing effect through securitization becomes even stronger under increasing statutory corporate income tax rates. In detail: A. Uhde, (2018), Tax Avoidance Through Securitization, Working Paper, April 3, mimeo.

Taking into account variables for the costs of financial distress, corporate liquidity, and capital market and macroeconomic conditions.

The focus in this section is on income taxation, but note that financial institutions pose challenging problems regarding their exemption in the VAT system as well, such that they invariably receive special treatment that is generally preferential and always non-neutral.

D.A. Schackelford, D.N. Shaviro, and J. Slemrod, (2010), Taxation and the Financial Sector, National Tax Journal, Vol. 63, Issue 4, Part 1, pp. 781–806. They refer to the fact that declining real estate prices would not have created such widespread mortgage default risk had not loan-to-value ratios been so high (p. 784).

See in extenso: S. Fatica, T. Hemmelgarn, and G. Nicodème, (2012), The Debt-Equity Tax Bias: Consequences and Solutions, EC Taxation Papers Series, Working Paper Nr. 33, whoconclude that tax deductibility of interest payments in most corporate income tax systems coupled with no such measure for equity financing creates economic distortions and exacerbates leverage. Leverage increases with the Corporate Income Tax (CIT) rate. The reason is that the statutory CIT rate determines the value of the debt capital structure of banks.

E.D. Kleinbard, (2003), Competitive Convergence in the Financial Services Market. Taxes, Vol. 81, Issue 3, pp. 225–260. See also S. Langedijk et al., (2014), Debt Bias in Corporate Taxation and the Costs of the Banking Crisis in the EU, EC Taxation Paper Series, Nr. 50, who evidence that eliminating the tax bias could lead to a reduction of public finance losses of around 60–90% (given a certain level/bandwidth of bank leverage elasticity) caused by the 2008 financial meltdown. The abolition of the preferential treatment is therefore argued as a tool to complement regulatory reform.

T. Hemmelgarn and D. Teichmann, (2013), Tax Reforms and Capital Structure of Banks, EC Taxation Papers Series, Working Paper Nr. 37. They conclude that leverage increases with the CIT rate. The reason is that the statutory CIT rate determines the value of the debt tax shield. A higher tax rate increases incentives to use debt finance when interest payments are deductible from the CIT base. Their results suggest that future tax policies should focus on eliminating the favorable treatment of debt for banks. The reason is that this distortion at least partly undermines the objective of increasing regulatory capital in the financial sector.

S. Langedijk et al., (2014), Debt Bias in Corporate Taxation and the Cost of the Banking Crisis in the EU, EC Taxation Working Papers Nr. 40, October; O. Luca and A. Tieman, (2016), Financial Sector Debt Bias, IMF Working Paper Nr. 16/217, November. Most tax systems create a tax bias toward debt finance. Such debt bias increases leverage and may negatively affect financial stability. The latter find the debt bias to be pervasive, explaining as much as 10% of total leverage for regular banks and 20% for investment banks.

This is true across the board, that is, for financial and non-financial companies, for small and large companies, for domestic and international companies. The strength of this effect (the debt bias contributing to leverage) differs with firm size, the availability of collateral, income and income volatility, cash flow, and capital intensity. See in detail: P. Dallari et al., (2018), Pouring Oil on Fire: Interest Deductibility and Corporate Debt, IMF Working Paper Nr. WP/18/257, December. Also: A. Auerbach et al., (2017), Destination-Based Cash Flow Taxation, Working Paper Nr. 17/01 (Oxford, United Kingdom: Oxford University Centre for Business Taxation); J. Bluedorn, and C. Ebeke, (2016), Investment, Firm Size, and the Corporate Debt Burden: A Firm-Level Analysis of the Euro Area, IMF Working Paper Nr. WP/16/200; M. Faulkender and J. M. Smith, (2016), Taxes and Leverage at Multinational Corporations, Journal of Financial Economics, Vol. 122, Issue 1, pp. 1–20; R.A. De Mooij, and M. Keen, (2016), Debt, Taxes, and Banks, Journal of Money, Credit, and Banking, Vol.48, pp. 5–33; J.H. Heckemeyer, and R. A. de Mooij, (2017), Taxation and Corporate Debt: Are Banks Any Different? National Tax Journal, Vol. 70, Issue 1, pp. 53–76; O. Luca, and A. F. Tieman, (2016), Financial Sector Debt Bias, IMF Working Paper Nr. WP/16/217, Washington, DC.

O. Luca and A. Tieman, (2016), Financial Sector Debt Bias, IMF Working Paper Series Nr. WP/16/217, November. Also: J. Bluedorn and C. Ebeke, (2016), Investment, Firm Size, and the Corporate Debt Burden: A Firm-Level Analysis of the Euro Area, in Euro Area Policies Selected Issues Papers, IMF Country Report Nr. 16/220; R. A. De Mooij, and M. Keen, (2016), Debt, Taxes, and Banks, Journal of Money, Credit, and Banking, Vol 48, pp. 5–33; P.B. Sorensen, (2014), Taxation and the Optimal Constraint on Corporate Debt Finance, Working Paper Nr. 5101, CESifo, Munich, Germany.

On the asset side the impact depends on the financial strength of the bank: well-capitalized banks respond to a reduction in tax rates by increasing their assets, but poorly capitalized banks respond by cleaning up their balance sheet. See in detail: L. Gambacorta et al., (2017), The Effects of Tax on Bank Liability Structure, BIS Working Paper Nr. 611, February. Also: G. Schepens, (2016), Taxes and Bank Capital Structure, Journal of Financial Economics, Vol. 120, Issue 3, pp. 585–600.

D. A. Schackelford, D. N. Shaviro, and J. Slemrod, (2010), Taxation and the Financial Sector, National Tax Journal, Vol. 63, Issue 4, Part 1, p. 785.

Backward-looking taxes are, however, not the least distortionary way to raise revenue in comparison to an appropriately designed Pigovian tax.

Both at the level of individual entities as well as at the level of macro-level. See for details: Z. Alla et al., (2018), Macroprudential Stress Tests: A Reduced-Form Approach to Quantifying Systemic Risk Losses, IMF Working Paper Nr. WP/18/49, March. A distinction is made between individual entity losses and losses resulting from systemic risk effects (SE losses). SE losses are measured using a reduced-form model to value financial entity assets, conditional on macroeconomic stress and the distress of other entities in the system. SE losses capture the effects of interconnectedness structures that are consistent with markets’ perceptions of risk. SE losses can be decomposed into the likelihood of distress and the magnitude of losses, thereby quantifying the contribution of specific entities to systemic contagion (pp. 18–22). Literature regarding systemic risk, quantifying systemic risk and amplification techniques is building up. See, for instance: V.V. Acharya et al., (2017), Measuring Systemic Risk, Review of Financial Studies, Vol. 30, Issue 1, pp. 2–47; R. Anderson et al., (2018), Macroprudential Stress Test and Policies: In Search of an Implementable and Robust Framework, IMF Working Paper Nr. WP/18/197, September; R. Espinoza et al., (2020), Systemic Risk Modeling: How Theory Can Meet Statistics, IMF Working Paper Nr. WP/20/54, International Monetary Fund, Washington, DC., March; M. Segoviano and R. Espinoza, (2017), Consistent Measures of Systemic Risk, LSE Systemic Risk Centre, DP 74, London.

E.D. Domar, and R. A. Musgrave, (1944), Proportional Income Taxation and Risk- Taking, Quarterly Journal of Economics, Vol. 58, Issue 3, pp. 388–422.

See regarding incentive issues in the FI sector Schackelford et al., Ibid. pp. 787–788.

V.V. Acharya and M. Richardson, (2009), Causes of the Financial Crisis. Critical Review, Vol. 21, Issue 2–3, pp. 195–210.

D.J. Elliott, (2010), Tax Policy and Bank Regulation, Manuscript, Brookings Institution, Washington, DC, unpublished. The first consists of investors with savings that they want to invest in a safe and highly liquid manner, permitting them to withdraw cash whenever they like. The second consists of businesses that want to use this pool of savings to fund investment projects that may be riskier and are more long-term, requiring a relatively illiquid commitment of funds.

See in extenso: I. Aldasoro et al., (2016), Bank Networks: Contagion, Systemic Risk and Prudential Policy, BIS Working Paper Nr. 597, December; I. Vodenska et al., (2017), Systemic Risk and Vulnerabilities of Bank Networks, Working Paper, mimeo. They propose a model for systemic risk propagation based on common bank exposures to specific asset classes. They analyzed the dynamics of tier 1 capital ratio after stressing the bank network and find that while the system is able to withstand shocks for a wide range of parameters, we identify a critical threshold for asset risk beyond which the system transitions from stable to unstable.

Schackelford et al., Ibid. p. 792.

M. Famè and A. Vouldis, (2017), Business Models of the Banks in the Euro Area, ECB Working Paper Nr. 2070, May. Typically, four types of banking business models are identified, that is, traditional commercial, complex commercial, wholesale funded and securities holding banks are present, alongside with specialized institutions such as state-owned entities aimed at refinancing loans to semi-public and public entities. Performance, efficiency and risk characteristics are materially different across the four business models (p. 39 for details). A. Lucas et al., (2017), Bank Business Models at Zero Interest Rates, ECB Working Paper Nr. 2084 June. Lucas et al. work with six categories in terms of business models: large universal banks, including globally systemically important banks (G-SIBs), international diversified lends, fee-based banks, domestic diversified lenders, domestic retail lenders and small international banks. They also conclude ‘as long-term interest rates decrease, banks on average (across all business models) tend to grow larger, hold more assets in trading portfolios to offset declines in loan demand, hold more sizeable derivative books, and, in some cases increase leverage and decrease funding through customer deposits (pp. 2ff.)’. Global systemically important banks (GSIBs) and global systemically important insurers (GSIIs) appear to be main sources of market-based connectedness. Total system connectedness—that is, among all GSIBs and GSIIs—tends to rise during financial stress, which is corroborated by a balance sheet oriented systemic risk measure. See in detail: S. Malik and T. Xu, (2017), Interconnectedness of Global Systemically Important Banks and Insurers, IMF Working Paper Nr. WP/17/210, September.

T. Adrian and M. K. Brunnermeier, (2008), CoVaR, Staff Report Nr. 348, Federal Reserve Bank of New York, New York, NY. Later updated as: T. Adrian, and M.K. Brunnermeier, (2016), ‘CoVar’, American Economic Review, Vol. 106, Nr. 7, July, pp. 1705–1741. See for a variation in Islamic equity markets: S. Braiek et al., (2018), Systemic Risk Contribution in Islamic Equity Markets: CoVaR Based Model, University of Sousse Working Paper, January, mimeo; A. Di Clemente, (2018), Estimating the Marginal Contribution to Systemic Risk by a CoVaR-Model Based on Copula Functions and Extreme Value Theory, Economic Notes, Vol. 47, Issue 1, pp. 69–112. See applied: L. Wang, (2017), Bank Rating Gaps as Proxies for Systemic Risk, University of Alberta Working Paper, mimeo.

Schackelford et al., Ibid. p. 793.

Even if the social harm could be reduced to the fiscal cost of supplying insurance protection, the government would hold a risky position, as the insurer of all financial firms, if their various investment risks (such as from betting against a downturn in real estate prices) were correlated. Thus, even with an actuarially fair fee the insurance fund would have a large positive balance when tail risk was realized less frequently than expected and would leave the government with financial exposure under the opposite scenario, Schackelford et al., Ibid. p. 793.

D.J. Elliott, (2010), A Primer on Bank Capital, The Brookings Institution, Washington, D.C.

See in detail Schackelford et al., Ibid. pp. 794–796. They further analyze the recently introduced bank levies given the fundamental objectives they have in common: ‘(1) a desire for retribution or recompense from parties deemed to have caused, and/or profited from, the recent crisis, (2) a desire to align private incentives with the social cost of activities that demonstrably have potentially catastrophic external contagion effects, so as to reduce the likelihood of future crises, and (3) a desire to raise revenue to offset the government fiscal imbalances exacerbated by the cost of dealing with the financial crisis and subsequent recession’ (p. 800).

If the quantity regulation of an activity that generates a negative externality is set optimally, then any tax set at a rate less than or equal to the marginal social damage will raise revenue, but will not alter behavior from the social optimum. Such a tax is attractive because it raises revenue with no distortion rather than because of its Pigovian incentives. A tax set at a rate in excess of the marginal social damage collects further revenue, but at the cost of moving the equilibrium from the social optimum, with too little of the externality-generating activity. If the quantity regulation is set too laxly, a tax at a rate below the implicit marginal social damage implied by the regulation will raise revenue with no marginal effect, while a tax in excess of that amount (but not greater than the actual marginal social damage) will both raise revenue and affect activity in the right direction. If the quantity regulation is set too strictly, a tax raises revenue without affecting behavior; neither would a subsidy offset the suboptimal level of activity, although it would incur a revenue loss, Schackelford et al., Ibid. p. 795)

See F. De Fiore, (2015), Corporate Debt Structure and the Financial Crisis, ECB Working Paper Series Nr. 1759, February, who focus on the amplification of banking distress on non-financial corporate debt and the amplification through the debt structure on balance sheets. US monetary policy and emerging market leverage are associated. See: A. Alter and S. Elekdag, (2016), Emerging Market Corporate Leverage and Global Financial Conditions, IMF Working Paper Nr. 16/243, December. Overall, increasing indebtedness and reducing productivity growth go hand-in-hand. See, for instance, for a case study: G. Anderson and M. Raisi, (2018), Corporate Indebtedness and Low Productivity Growth of Italian Firms, IMF Working Paper Nr. WP/18/33, February. They find significant negative effects of persistent corporate debt build-up on total factor productivity growth, and weak evidence of a threshold level of corporate debt, beyond which productivity growth drops off significantly. The latter most likely implies a gradual reduction of productivity and the fact that the debt accumulation build-up strangles firm productivity throughout the curve and not only at high or excessive levels of debt, which makes it a mainstream and across-the-board problem. A different design of the tax system could turn the tide. Also: L. Stépgane et al., (2018), Monetary Policy and Corporate Debt Structure, Banque de France Working Paper Nr. 697, October 17. Conventional and unconventional expansionary monetary policies have similar positive effects on aggregate activity, but their impact on corporate debt structure goes in opposite directions: (i) conventional monetary easing increases loans to non-financial corporations and reduces corporate bond financing; (ii) unconventional monetary easing increases bond finance without affecting the loans.

Colliard and Hofmann however measure no effect and support for the idea that an FTT improves market quality by affecting the composition of trading volume. Instead, their results are in line with the hypothesis that a lower trading volume reduces liquidity, and thereby market quality. See in detail: J.-E. Colliard and P. Hoffmann, (2017), Financial Transaction Taxes, Market Composition, and Liquidity, ECB Working Paper Nr. 2030, February (including literature list to be consulted in case of particular interest in theory design for FTT). They do however observe a shift in terms of securities from short-term to long-term investors. Their reference to the fact that an FTT qualifies as a Pigovian tax (p. 34) cannot be underwritten for the reasons indicated in this chapter. See for other detrimental aspects of a standard FTT pp. 33–35. They analyzed the impact of the French FTT that used to be in place. Also: G. Capelle-Blancard, et al., (2015), The Impact of the French Securities Transaction Tax on Market Liquidity and Volatility, RIETI Discussion Paper Series Nr. 14-E-007, January; P. Gomber, et al., (2016), Securities Transaction Tax and Market Quality - the Case of France, European Financial Management, Vol. 22, pp. 313–337.

T. Matheson, (2010), Taxing Financial Transactions: Issues and Evidence, Working Paper WP/11/54, International Monetary Fund, Washington, DC.

T. Hemmelgarn and G. Nicodème, (2010), The 2008 Financial Crisis and Taxation Policy, Working Paper Nr. 20-2010. European Commission, Luxembourg.

In extenso: P. Honohan and S. Yoder, (2010), Financial Transactions Tax: Panacea, Threat, or Damp Squib?, Policy Research Working Paper Series Nr. 5230. Development Research Group, The World Bank, Washington, DC.

See also: J. Ha, (2015), What Explains Volatility Spillovers in the Financial Systems of Emerging Market Countries: Co-movement, Transmission, or Contagion?, Cornell Working Paper, September version, mimeo. He concludes besides the fact that volatility spillovers between the foreign exchange and stock markets are significant in most EM countries, that such spillovers are found to be contingent on the sample period and market conditions. He also examines the sources of volatility spillovers, namely, co-movement through common information, sequential information transmission, and contagion. Common approaches used to estimate interest rate spillovers tend to understate the degree of monetary autonomy enjoyed by small open economies with flexible exchange rates. See: C. Caceres et al., (2016), Global Financial Conditions and Monetary Policy Autonomy, IMF Working Paper Nr. 16/108, June.

See regarding the analysis of systemic risk: D. Bisias et al., (2012), A Survey of Systemic Risk Analytics, U.S. Department of the Treasury, Office of Financial Research, Working Paper Nr. 1, January 5. They provide a review of 31 quantitative measures of systemic risk that occurred in the financial and economics literature.

Voellmy argues that shadow banks issue only uninsured deposits while commercial banks issue both insured and uninsured deposits. The effect of shadow banking on financial stability is ambiguous and depends on the (exogenous) upper limit on insured deposits. If the upper limit on insured deposits is high, then the presence of a shadow banking sector is detrimental to financial stability. Shadow banking then creates systemic instability that would not be present if all deposits were held in the commercial banking sector. In contrast, if the upper limit on insured deposits is low, then the presence of a shadow banking sector is beneficial from a financial stability perspective. Shadow banks absorb uninsured (and uninsurable) deposits from the commercial banking sector, thereby shielding commercial banks from runs. In detail: L. Voellmy, (2019), Shadow Banking and Financial Stability under Limited Deposit Insurance, Working Paper, March 21, mimeo.

W. Wagner, (2010), In the Quest of Systemic Externalities: A Review of the Literature, CESifo Economic Studies, Vol. 56, pp. 96–111.

M. Bijlsma, M. Lever, J. Anthony and G. Zwart, (2011), An Evaluation of the Financial Transaction Tax, CPB Background Paper, p. 6. For a re-evaluation of the FTT in terms of its impact on informational efficiency, liquidity and volatility, see: M. Cipriani et al., (2019), Financial Transaction Taxes and the Informational Efficiency of Financial Markets: A Structural Estimation, Systemic Risk Centre Discussion Paper Nr. 88, March 1. The introduction of an FTT changes the composition of the market, lowering informational efficiency. Even a small, 5 bps FTT impedes correct price convergence on a sizeable percentage of days.

J. Scheinkman and W. Xiong, (2003), Overconfidence and Speculative Bubbles, Journal of Political Economy, Vol. 111, pp. 1183–1219.

D.P. Porter and V. L. Smith, (2003), Stock Market Bubbles in the Laboratory, The Journal of Behavioral Finance, Vol. 4, Issue 1, pp. 7–20.

Reinhart and Rogoff argue that other tools may do better at preventing real estate bubbles. Banking regulators may for example use capital buffers to reduce excessive lending or set minimum collateral requirements for mortgages. K. Rogoff and C. Reinhart, (2009), This Time is Different: Eight Centuries of Financial Folly, Princeton University Press, Princeton.

M. Bijlsma, M. Lever, J. Anthony, and G. Zwart, (2011), An Evaluation of the Financial Transaction Tax, CPB Background Paper, (2011), illustrate: ‘when the acquisition concerns information that will anyway be publicly revealed soon, but speculators can earn a profit by getting their hands on the information early’…‘But even when such information acquisition is in itself socially beneficial (e.g., long-term fundamental information on the security), duplication of collection efforts need not be efficient. A tax on transactions can then efficiently reduce the incentives to invest in those efforts’ (pp. 7–8).

L.H. Summers and V.P. Summers, (1989), When Financial Markets Work Too Well: A Cautious Case for a Securities Transactions Tax, Journal of Financial Services, Vol. 3, pp. 261–286. It convinced Stiglitz already decades ago to suggest a transactions tax, See: J.E. Stiglitz, (1989), Using Tax Policy to Curb Speculative Short-Term Trading, Journal of Financial Services, Vol. 3, Issue 2&3, pp. 101–113.

S.J. Grossman and J.E. Stiglitz, (1980), On the Impossibility of Informationally Efficient Markets, American Economic Review, Vol. 70, Issue 3, pp. 393–408.

The reason is that the willingness of one agent to buy at a price p reveals to other traders that this agent has information that the actual value of the good is higher than p. Other traders then prefer to hold on to the good themselves, and the informed agent cannot benefit from the information advantage. Hence, there will be too little investment in information acquisition, Bijlsma et al., Ibid. p. 8.

D. Fudenberg and J. Tirole, (1991), Game Theory, MIT Press, Cambridge, Sect. 14.3.3.

The noise that these traders introduce into the price formation process allows informed traders to make a trading profit under the cover of the liquidity trades. In addition, models may also explicitly describe the price formation process, for example by including market makers or arbitrageurs who do not possess any information themselves, but who try to disentangle the informed trades from the noise and try to capture some of the profits from that information, see in detail: A. Kyle, (1985), Continuous Auctions and Insider Trading, Econometrica, Vol. 53, Issue 6, pp. 1315–1335.

See for an empirical model: J. Dow and R. Rahi, (2000), Should Speculators Be Taxed?, Journal of Business, Vol. 73, Issue 1, pp. 89–107. Results seem to imply that taxation reduces the amount of trade on information, making prices less informative. The latter effect is beneficial for speculators: they get higher revenue on their informed trades. Subramanyam compares long- and short-term investors and concludes that ‘that taxes indeed increase the incentives of agents to acquire long-term information over short-term information.’ A. Subrahmanyam, (1998), Transaction Taxes and Financial Market Equilibrium, Journal of Business, Vol. 71, Issue 1, pp. 81–117.

They studied the effect of a tax on uninformed arbitrageurs in a laboratory experiment. Arbitrageurs exploit a market inefficiency, that is, they buy identical or very similar assets in different markets (often in different time zones) exploiting the mispricing between those two markets. These uninformed actors trade with informed traders (speculators) and with liquidity traders (who need to trade for exogenous reasons). They find that when only the arbitrageurs’ trades are taxed, they trade less, and lose less money on their trades. The information content of prices remains unchanged. However, their experiment does not shed light on the incentives of speculators to acquire information, nor does it take into account the effect of taxes on liquidity traders’ presence in the market, R. Bloomfield, M. M. O’Hara and G. Saar, (2009), How Noise Trading Affects Markets: An Experimental Analysis, Review of Financial Studies, Vol. 22, pp. 2275–2302. (Also Bijlsma et al., Ibid. p. 9)

See in extenso: S. Gabrieli and C.-P. Georg, (2014), A Network on Interbank Freezes, Deutsche Bank Discussion Paper, Nr. 44/2014. See for a complete review of the literature on interbank networks: A.-C. Hüser, (2015), Too Interconnected to Fail: A Survey of the Interbank Networks Literature, SAFE Working Paper Nr. 91. She presents a systematic overview of our current understanding of the structure of interbank networks, how network characteristics affect contagion in the banking system and how banks form connections when faced with the possibility of contagion and systemic risk. See also: M.R.C. van Ooordt and C. Zhou, (2016), Estimating Systemic Risk under Extremely Adverse Market Conditions, Bank of Canada Working Paper Nr. 22, May. See for some history on the role of interbank networks: C.W. Calomiris and M. Carlson, (2016), Interbank Networks in the National Banking Era: their Purpose and Their Role in the Panic of 1893, BIS Working Paper Nr. 535, January. From the perspective of systemic stability a sparsely connected network is preferred over a more densely connected network, even if the densely connected network is more stable in the absence of intervention. The optimal bail-in plan requires lower taxpayer contributions in more sparsely connected networks. See: B. bernadrd et al., (2017), Bail-Ins and Bail-Outs: Incentives, Connectivity and Systemic Stability, Working paper, April 11, mimeo. Aldaroso and Alves depict the European interbanking sphere. They break the network down by both maturity and instrument type. Interesting as well is the understanding that banks that are well connected in certain networks tend to do so in other networks as well (positively correlated multiplexity). Also the fact that they developed a model to identify systemic importance across multiple layers of different networks simultaneously. See in detail: I. Aldaroso and I. Alves, (2017), Multiplex Interbank Networks and Systemic Importance- an Application to European Data, BIS Working Paper Nr. 603, January. Also: L. Bargigli, et al., (2015), The Multiplex Structure of Interbank Networks. Quantitative Finance, Vol. 15, Issue 4, pp. 673–691; M. Bluhm, et al., (2016), Interbank Intermediation, Deutsche Bundesbank Working Paper Nr. 2016-16; E. Denbee et al., (2018), Network Risk and Key Players: A Structural Analysis of Interbank Liquidity, Working Paper, February 5, mimeo.

See for their findings: D. Grigat and F. Caccioli, (2017), Reverse Stress Testing Interbank Networks, Scientific Reports, Vol. 7, Issue 1, Article number: 15616.

Ramadiah et al. find that the observed credit network significantly displays the highest systemic risk level. They use data on bank-firm credit relationships (in Japan) and conduct a horse race between different network reconstruction methods in terms of their ability to reproduce the actual credit networks. They then compare the different reconstruction methods in terms of their implied systemic risk levels. See in detail: A. Ramadiah et al., (2018), Reconstructing and Stress testing Credit Networks, ESRB Working Papers Nr. 84, September 18.

Also known as noise traders: those traders who are buying or selling securities for a variety of unpredictable reasons rather than reasons based on informed decision-making.

See in detail: D. Gabor, (2016), A Step Too Far? The European Financial Transaction Tax and Shadow Banking, Journal of European Public Policy, Vol. 23, Issue 6, pp. 925–945.

F. Guarascio, (2015), EU Deal on Financial Transaction tax ‘Within Reach’, Reuters.​com, September 12.

See in extenso: T. Adrian et al., (2014), Financial Stability Monitoring, Federal Reserve Bank of NY, Staff Reports, Nr. 601, revised edition June 2014, in particular pp. 16–21.

Function-specific payments, industry-specific payments and more advanced models that are based on derivative ‘contract for difference techniques’. See also: R. Albuquerque et al., (2018), Relative Performance, Banker Compensation, and Systemic Risk, European Corporate Governance Institute (ECGI) - Finance Working Paper Nr. 490/2016 (updated), January.

Schackelford et al., Ibid. p. 798.

See for an actual comparative analysis on bank levies: M. Dowell-Jones and R. Buckley, (2015), Bank Levies: An Innovation in Post-Crisis Bank Taxation, Working Paper, mimeo.

Schackelford et al., Ibid. pp. 798–799.

S. Claessens, M. Keen and C. Pazarbasioglu, (2010), Financial Sector Taxation, The IMFs Report to the G20 and Background material, Washington, Washington DC.

E.D. Kleinbard and T. Edgar, (2010), The Financial Sector and the Crisis: Was Tax the Problem? Is It the Solution? Slides presented at a workshop on ‘Rethinking the Taxation of the Financial Sector in Light of the Recent Crisis,’ February 5.

This argument addresses the fact that a tax on pure profits or rents in any sector has desirable efficiency properties, but in practice it is difficult to separate pure profits or rents from the normal return to capital, and so designing a non-distorting tax is difficult. If one can assert that most of the observed return in a given sector is rent or pure profit, then the potential distorting effect per dollar raised is arguably low, Schackelford et al., Ibid., p. 799, endnote 17.

Such a tax would reduce risk taking given that the tax treatment of losses is punitively asymmetric. Otherwise, a tax on profits might induce decision makers to increase pre-tax risk positions in order to restore the after-tax positions they had in the absence of the tax. Note also that ex post increases in the generosity of the tax treatment of losses are a form of taxpayer bailout, Schackelford et al. Ibid. p. 800, endnote 18.

See, for instance,

S. Ben Hadj, (2014), Financial Institutions Externalities and Systemic Risk: Tales of Tails Symmetry, UCL Working Paper, November. He analyses the contribution of banks to the systemic risk based on the asymmetry of the right and left tails of the loss distribution. The basic idea is that a bank should create as much risk as it undertakes. Any imbalance in the distribution of profit and losses is a sign of the bank’s failure to internalize its externalities or the social costs associated to its activities. Tail symmetry and sustainability of the financial system are obviously interwoven.

Using country differences on double taxation in multinational banks, they show that (1) a large fraction (85%) of such taxes are reflected in prices, and (2) banks adjust their behavior (activities in different countries) to a large extent in response to such tax differences. Huizinga et al. claim that also FAT may suffer from such pass-through rates, and recommend that any such taxes are harmonized across the EU, and across different parts of the financial sector; see H. Huizinga, J. Voget, and W. Wagner, (2011), International Taxation and Cross-Border Banking, European Banking Center, Discussion paper, Nr. 2011-015.

See in extenso: K. Sadiq, (2014), Unitary Taxation of the Finance Sector, International Center for Tax and Development, Working Paper Nr, 25, November.

International Monetary Fund, (2010), A Fair and Substantial Contribution by the Financial Sector, Interim Report for the G-20. International Monetary Fund, Washington, DC, p. 26.

The identification and quantification of the systemic component of financial risk require an in-depth understanding of the channels through which shocks can spread and amplify, thereby jeopardizing the stability of a financial system. A holistic view is troublesome as that requires an understanding of the key determinants of the interconnectedness among FIs. Abbassi et al. use CDS prices to measure market-implied interconnectedness (to evaluate how much market information-based measures capture actual balance sheet linkages and risks associated with banks’ funding, security investment and credit provision behavior). As a consequence they examine the relationship between market-based measures of credit risk interconnectedness and actual common exposures of banks through their funding and securities holdings. They conclude that the market-based interconnectedness measure strongly reflects the information on banks’ exposure to the wholesale funding market and assets associated with securities investments and credit supply. Second, they show that the relation between our market-based interconnectedness measure and the balance sheet positions exhibits asymmetries both over time and cross-sectionally. See in detail: P. Abbassi et al., (2016), Credit Risk Interconnectedness: What Does the Market Really Know, Deutsche Bundesbank Discussion Papers Nr. 9, January (later on published in Journal of Financial Stability, Vol. 29, pp. 1–12). Also P. Giudici, (2017), Sovereign risk in the Euro area: a multivariate stochastic process approach, Journal of Quantitative Finance, Vol. 17, Nr. 12, pp. 1995–2008; C. Brownlees, (2015) Bank Credit Risk Networks: Evidence from the Eurozone Crisis, Working Paper, mimeo; BIS, (2016), Annual Report 86th Edition, V. Towards a Financial Stability-Oriented Fical Policy, pp. 93–94. Technology and easy and quick news reduces trend-following behavior: B. Eichengreen et al., (2017), Thick v. Thin-Skinned: Technology, News and Financial market Reaction, IMF Working Paper Nr. WP/17/91, April; F. Diebold, and K. Yilmaz, (2015), Financial and Macroeconomic Connectedness, Oxford University Press, Oxford. Diebold and Yilmaz introduced variance decomposition networks as tools for quantifying and ranking the systemic risk of individual firms. The nature of these networks and their implied rankings depend on the choice decomposition method. There is a standard one known as the ‘order invariant generalized forecast error variance decomposition’ (H.H. Pesaran and Y. Shin, (1998), Generalized Impulse Response Analysis in Linear Multivariate Models, Economic Letters, Vol. 58, Nr. 1, pp. 17–29). In short, the problem is that the error variation doesn’t add to unity and so comparison or ranking become impossible. The model suggested by Lanne-Nyberg (M. Lanne and H. Nyberg, (2016), Generalized Forecast Error Variance Decomposition for Linear and Nonlinear Multivariate Models, Oxford Bulletin of Economics and Statistics, Vol. 78, Nr. 4, pp. 595–603) is known as the order invariance property sharing model. Caution is still warranted when using rakings and risk contributions for guiding financial regulation and economic policy. See in detail: J.A. Chau-Lau, (2017), Variance Decomposition Networks: Potential Pitfalls and a Simple Solution, IMF Working Paper Nr. WP/17/107, May.

See also: D. Schoenmaker and S. Oosterloo, (2005), Financial Supervision in an Integrating Europe: Measuring Cross-Border Externalities, International Finance, Vol. 8, Issue 1, pp. 1–27.

See for example: A.R. Admati, (2000), Forcing Firms to Talk: Financial Disclosure Regulation and Externalities, Review of Financial Studies, Vol. 13, Issue 3, pp. 479–519.

For extensive discussions of the concept of systemic risk and comprehensive literature see O. de Bandt and P. Hartmann, (2000), Systemic Risk: A Survey, ECB Working Paper Series, Nr. 35, and O. de Bandt, P. Hartmann and J. Peydró, (2009), Systemic Risk in Banking: An Update, ECB Working Paper Series and in A. Berger, P. Molyneux and J. Wilson (eds.), (2012), Oxford Handbook of Banking, Oxford University Press, Oxford. See further: V. Acharya, et al., (2017), Measuring Systemic Risk, Review of Financial Studies, Vol. 30, Issue 1, pp. 2–47; S. Benoit, et al., (2017), Where the Risks Lie: A Survey on Systemic Risk, Review of Finance, Vol. 21, pp. 1, pp. 109–152.

P. Hartmann, (2009), Systemic Risk, ECB Financial Stability Review, December, pp. 134–142.

See: E. Cerutti, S. Claessens and P. McGuire, (2012), Systemic Risk in Global Banking: What Can Available Data Tell Us and What More Data are Needed, BIS Working Papers, Nr. 376; G. Hale et al., (2016), Crisis Transmission through the Global Banking Network, IMF Working Paper Nr. 16/91 April. Also: E. Cerutti and H. Zhou, (2017), The Global Banking Network in the Aftermath of the Crisis: Is There Evidence of De-globalization?, IMF Working Paper Nr. WP/17/232, November. Post-crisis dynamics show a shrinkage in the overall amount of cross-border bank lending is the traditional conclusion. But not so fast. Cerutti and Zhou claim: while the global banking network has evolved it has not undergone a generalized retrenchment in financial linkages. In fact, some parts of the network are currently more interlinked regionally than before the crisis and less dependent on major global lenders. They confirmed this trend and detailed it later on in E. Cerutti and H. Zhou, (2018), The Global Banking Network: What is Behind the Increasing Regionalization Trend?, IMF Working Paper Nr. WP/18/46, March. They confirm the regionalization trend and conclude that ‘[d]espite the large decline in aggregate cross-border banking lending volumes, some parts of the global banking network are currently more interlinked regionally than before the Global Financial Crisis’. They developed a simple theoretical model capturing banks’ internationalization decisions, and the outcome shows that this regionalization trend is present even after controlling for traditional gravitational variables (e.g. distance, language, legal system, etc.). In fact they argue, ‘this regionalization trend was present before the 2008 financial crisis, but it has increased since then, and it seems to be associated with regulatory variables (i.e. the dampening of some barriers to direct cross-border and local affiliate lending) and the opportunities created by the retrenchment of several European lenders’ (pp. 6–7, 31–36). See in favor: J. Caruana, (2017), Have We Passed ‘Peak Finance’? Lecture, International Center for Monetary and Banking Studies; E. Cerutti and S. Claessens, (2017), The Great Cross-Border Bank Deleveraging: Supply Constraints and Intro-Group Frictions, Review of Finance, Vol. 21, pp. 201–236. Csonto et al. assess the systemic vulnerabilities in the EU banking system using two stress tests: (1) solvency and liquidity shocks to each individual bank and the impact on other banks in the network through their bilateral net asset exposures, (2) country and region-wide tail shocks to GDP affecting capital and liquidity of all banks in the shocked jurisdictions, followed by the rippling effects through the regional network. See in detail: B. Csonto et al., (2018), Bank Network Analysis in the ECCU, IMF Working Paper Nr. WP/18/162, July. The end result is an identification of systemically important institutions, judged by the ‘degree of connectivity ‘with the rest of the system, and the extent to which they are ‘vulnerable to the failure of other banks.’ See also: A. Myrovda and J. Reynaud, (2018), Monetary Policy Transmission in the ECCU, IMF Working Paper WP/18/70. Also: J.E. Anderson et al., (2017), Short Run Gravity, NBER Working Paper Nr. 23,458; E. Cerutti and S. Claessens, (2017), The Great Cross-Border Bank Deleveraging: Supply Constraints and Intra-Group Frictions, Review of Finance, Vol. 21, Nr. 1, pp. 201–236.

See F. Fecht, H.P. Grüner and P. Hartmann, (2012), Financial Integration, Specialization and Systemic Risk, ECB Working Paper Series, Nr. 1425. Also: P. R. Lane and G.M. Milesi-Ferretti, (2017), International Financial Integration in the Aftermath of the Global Financial Crisis, IMF Working Paper Nr. WP/17/115, May. The latter finds that the growth in cross-border positions in relation to world GDP has come to a halt (covered period 1970–2015). This reflects much weaker capital flows to and from advanced economies, with diminished cross-border banking activity, and an increase in the weight of emerging economies in global GDP, as these economies have lower external assets and liabilities than advanced economies.

A tough one still is to identify contagion in a network or in particular a banking network. Morrison et al. studied the impact of trading profits and losses on the borrowing costs of the banks’ counterparties using derivative data (CDS). They used the network of CDS transactions between banks to identify bank CDS returns attributable to counterparty losses. That makes sense: ‘[a]ny bank’s exposure to corporate default increases whenever counterparties from whom it has purchased default protection themselves experience losses.’ In line with that understanding they document an increase in the CDS spread of such a bank. No such effect occurs from losses of non-counterparties. Interesting is that they further observed that the effect on bank CDS returns through this counterparty loss channel is sizeable relative to the direct effect on a bank’s CDS returns following on trading losses. See in detail: A. Morrison et al., (2017), Identifying Contagion in a Banking Network, Bank of England Staff Working Paper Nr. 642, January.

F. Allen and D. Gale, (2000), Financial Contagion, Journal of Political Economy, Vol. 108, Issue 1, pp. 1–33; X. Freixas, B. Parigi and J.C. Rochet, (2000), Systemic Risk, Interbank Relations and Liquidity Provision by the Central Bank, Journal of Money, Credit and Banking, Vol. 32, Issue 3, pp. 631–638.

H. Minsky described how in good times consumption and investment increase generates income, which fuels the financing of more consumption and investment but also the neglect of increasing risks. Even small events can then lead to a re-pricing of risk and an endogenous unraveling of the credit boom, which then adversely affects many intermediaries and markets at the same time; see H. Minsky, (1977), A Theory of Systemic Fragility, in E. Altman, and A. Sametz (eds.), Financial Crises: Institutions and Markets in a Fragile Environment, Wiley & Sons, Hoboken, pp. 138–152; C. Kindleberger, (1978), Manias, Crashes and Panics: A History of Financial Crises, Macmillan, Basingstoke.

G. Gorton, (1988), Banking Panics and Business Cycles, Oxford Economic Papers.

The vulnerability of financial markets is caused by (1) the information intensity and inter-temporal nature of financial contracts, (2) the balance sheet structures of financial intermediaries (often exhibiting high leverage and maturity mismatches), and (3) the high degree of interconnectedness of wholesale financial activities; Hartmann, (2009), Ibid. p. 135.

Financial systems allocate funds from agents who have them but possess no specific knowledge about promising investment opportunities, to agents who have knowledge about the opportunities but not the funds to engage in them. This creates an agency problem between the two parties, which may be handled more or less well through the underlying financial contracts. If contracts are incomplete and negative news arrive on some of the investment projects, but information asymmetries do not allow lenders to judge whether this also affects other investment projects, funding may evaporate for all projects alike.

J. Trichet, (2009), Systemic Risk, Clare Distinguished Lecture in Economics and Public Policy, University of Cambridge December 10.

See, for instance, G. Covi et al., (2019), CoMap: Mapping Contagion in the Euro Area Banking Sector, IMF Working Paper Nr. WP/19/102, May. They document the degree of interconnectedness and systemic risk of the euro area banking system based on bilateral linkages. They further developed a contagion mapping model fully calibrated with bank-level data to study the contagion potential of an exogenous shock via credit and funding risks. They observe that tipping points shifting the euro area banking system from a less vulnerable state to a highly vulnerable state are a non-linear function of the combination of network structures and bank-specific characteristics. Also: S. Basu et al. (2017), A System-Wide Approach to Measure Connectivity in the Financial Sector, Working Paper, mimeo; P. Glasserman and H. P. Young, (2016), Contagion in Financial Network, Journal of Economic Literature Vol. 54, Issue 3, pp. 779–831.

Financial imperfections have macroeconomic implications. They occur mainly through two channels: The first channel, which operates ‘through the demand side of finance and is captured by financial accelerator-type mechanisms, describes how changes in borrowers’ balance sheets can affect their access to finance and thereby amplify and propagate economic and financial shocks’. The second channel, which is associated with the supply side of finance, ‘emphasises the implications of changes in financial intermediaries’ balance sheets for the supply of credit, liquidity and asset prices, and, consequently, for macroeconomic outcomes’. See in detail: S. Claessens and M.A. Ayan Kose, (2017), Macroeconomic Implications and Financial Imperfections: a Survey, BIS Working Paper Nr. 677, November. They also determine the link between the real economy and the financial sector.

Initially developed by S. Calvo, and C.M. Reinhart, (1996), Capital Flows to Latin America: Is there Evidence of Contagion Effects?, World Bank Working Paper Nr. 1619.

Initially developed by T. Ahnert, and C. Bertsch, (2013), A Wake-up Call: Information Contagion and Strategic Uncertainty, Sveriges Riksbank Working Paper Series Nr. 282.

S. Vardanyan, (2016), Contagion in Experimental Financial Markets, Charles University CERGE-EI Working Paper Nr. 580, December, p. 2.

‘Bad news’ about an FI, or even its failure, or the crash of a financial market leads to considerable adverse effects on one or several other FIs or markets, for instance, their failure or crash. The essential feature is the ‘domino effect’ from one institution to the other and from one market to the other.

It includes not only the events described above but also severe and widespread (‘systemic’) shocks which adversely affect a large number of FIs or markets at the same time.

O. de Bandt and P. Hartmann, (2010), What is Systemic Risk Today?, Working Paper, p. 5. See also extensively regarding regulating systemic risk and the fact that systemic shocks are inevitable: S.L. Schwarcz, (2016), Perspectives on Regulating Systemic Risk, in Systemic Risk, Institutional Design and the Regulation of Financial Markets, (ed. Anita Anand), chapter two, Oxford University Press, Oxford. Also: S. L. Schwarcz, (2017), Rethinking Corporate Governance for a Bondholder Financed, Systemically Risky World, William & Mary Law Review, Vol. 58, pp. 1345–1374; S.L. Schwarcz, (2017), Controlling Systemic Risk through Corporate Governance, Center for International Governance Innovation Policy Brief Nr. 99, February. Excessive corporate risk taking and not reducing moral hazard and alignment between managers and investors should be the focus.

See de Bandt et al. (2010), Ibid. p. 5, for a full overview of the different categories.

See for an example analysis in credit markets: N. Boyarchenko et al., (2016), Trends in Credit Market Arbitrage, Federal Reserve Bank of New York Staff Reports, Nr. 784, July.

Hartmann, (2009), Systemic Risk, ECB Financial Stability Review, p. 135.

See also for a number of different hypotheses: C.-P. Georg, (2014), Contagious Herding and Endogenous Network Formation in Financial Networks, ECB Working Paper, Nr. 1700. Financial networks exhibit a core periphery network structure. But a core periphery network cannot be unilaterally stable when agents are homogeneous. A core periphery network structure can form endogenously, however, if allowed for heterogeneity among agents in size; see in detail: D. in ‘t Veld et al., (2015), The Formation of a Core periphery Structure in Heterogeneous Financial Networks, Risk and Macro Finance Working Paper Series Nr. 2015-1, University of Amsterdam. It is also a valid theory at the level of business cycles and the economy as such. SeeL A. Cesa-Bianchi et al., (2016), Finance and Synchronization, Bank of England Staff Working Paper Nr. 612, August.

K. Anand, B. Craig and G. von Peter, (2014), Filling in the Blanks: Network Structure and Interbank Contagion, BIS Working Paper, Nr. 455. Also: A. Liu et al., (2016), Interbank Contagion: An Agent-based Model Approach to Endogeneously Formed Networks, OFR Working Paper Nr. 16-14, December 20. Extrapolating latent interbank risk exposures is a tough job and many theories exist. Most of the models use highly stylized network models and reconstruction methods with global optimality lending allocation approaches such as maximizing entropy or minimizing costs. In reality however, Liu et al. argue, lending and borrowing decisions are largely suboptimal and performance-driven.

See in detail: K. Kim and S. Mitra, (2014), Real and Financial Vulnerabilities from Cross-Border Banking Linkages, IMF Working Paper, Nr. WP/14/136.

Insurance holdings with a diversified business mix of traditional life and non-life insurance business contribute less to systemic risk than monoline insurers, argue Kubitza and Regele. Based on a study covering 74 international insurance companies from 2007 to 2015 they conclude that on average insurance holdings with a fraction of slightly more than 50% of premiums written in life insurance exhibit the smallest contribution to systemic risk. This fraction tends to increase with an insurer’s investment volatility, leverage ratio and the scope of active reinsurance assumed; see in detail: C. Kubitza and F. Regele, (2018), Diversification of Business Activities and Systemic Risk, ICIR Working Paper Series Nr. 30/17, version January.

O. Butzbach, (2016), Systemic Risk, Macro-Prudential Regulation and Organizational Diversity in Banking, Journal for Policy and Society, Vol. 35, Issue 3: Institutional and Policy Design for the Financial Sector, pp. 239–251.

J. Bats and A. Houben, (2017), Bank-Based Versus Market-Based Financing: Implications for Systemic Risk, De Nederlandse Bank Working Paper, Nr. 577, December. See also: V. Acharya, et al., (2016), The Dark Side of Liquidity Creation: Leverage and Systemic Risk, Journal of Financial Intermediation, Vol. 28, pp. 4–21. Also: S. Von der Becke, (2015), Liquidity Creation and Financial Instability, ETH Zurich PhD thesis, mimeo. The thesis investigates how liquidity creation within the financial system can lead to financial crisis. Also: S. Claessens, (2016), Regulation and Structural Change in Financial Systems, CEPR Discussion Papers, Nr. 11822; S. Langfield, and M. Pagano, (2016), Bank Bias in Europe: Effects on Systemic Risk and Growth, Economic Policy, Vol. 31, pp. 51–106; S. Masciantonio and A. Zaghini, (2017), Systemic Risk and Systemic Importance Measures During the Crisis, Bank of Italy Working Paper, Nr. 1153.

See also: S. Benoit et al., (2017), Where the Risks Lie: A Survey on Systemic Risk, Review of Finance, Vol. 21, Issue 1, March 1, pp. 109–152. A multifactor approach to determining the SIFI nature is required as just focusing on size and assuming that larger banks pose more risks than smaller banks. See for a model: OFR, (2017), Size Alone is Not Sufficient to Identify Systemically Important Banks, OFR Viewpoint Nr. 4, October 26. Dávila and Walther show that bank size, purely on strategic grounds, is a key determinant of banks’ leverage choices, even when bailout policies treat large and small banks symmetrically. Large banks always take on more leverage than small banks because they internalize that their decisions directly affect the government’s optimal bailout policy. Small banks also choose strictly higher borrowing when large banks are present, since banks’ leverage choices are strategic complements. Overall, the presence of large banks increases aggregate leverage and the magnitude of bailouts. The optimal ex ante regulation features size-dependent policies that disproportionally restrict the leverage choices of large banks. In detail: E. Dávilla and A. Walther, (2017), Does Size Matter? Bailouts with large and Small Banks, NBER Working Paper Nr. 24132, December.

R. Gong and F. Page, (2016), Shadow Banks and Systemic Risks, SRC Discussion Paper Nr. 55, February. They base systemic risk on ‘the equilibrium state dynamics generated by a stochastic game of network formation with banks and shadow banks’.

Ibid. pp. 4–20 for the set-up and pp. 20–26 for the analysis.

See, for instance, D. Acemoglu, et al., (2015), Systemic Risk and Stability in Financial Networks, American Economic Review, Vol. 105, Nr. 2, pp. 564–608; L. Eisenberg, et al., (2001), Systemic Risk in Financial Systems, Management Science, Vol. 47, Nr. 2, pp. 236–249; M. Elliot et al., (2014), Financial Networks and Contagion, American Economic Review, Vol. 104, Nr. 10, pp. 3115–3153; R. Gong, et al., (2015), Endogenous Correlated Network Dynamics, SRC Working Paper Nr. 39, June; R. Gong and F. Page, (2016), Systemic Risk and the Dynamics of Temporary Financial Networks, SRC Working Paper Nr. 62, July.

Ibid. pp. 33–42 for the policy analysis.

The most recent version: Bank for International Settlements (BIS), (2013), Global Systemically Important Banks. Updated Assessment Methodology and the Higher Loss Absorbency Requirements. See for a scorecard analysis for the year 2013: BIS, (2014), The G-SIB Assessment Methodology- Score Calculation, (November 2014) and FSB, (2015), Thematic Review on Supervisory Frameworks and Approaches for SIBs. Peer Review Report, May 26. Fique analyzed the ‘additional loss absorbency requirement’ for G-SIBs from the perspective of a microfounded design of capital surcharges that target the interconnectedness component of systemic risk. These costs increase the cost of establishing interbank connections, which has a welfare effect. Two conclusions were drawn: (1) [w]hile reduced interconnectedness decreases welfare by restricting the ability of banks to insure against liquidity shocks, it also increases it by reducing contagion when an interconnected bank fails. The regulator faces a trade-off between efficiency and financial stability; and (2) capital requirements are more effective than default fund contributions when tail-risk exposure is the private information of banks. See in detail: J. Fique, (2016), A Microfounded Design of Interconnectedness-Based Macroprudential Policy, Bank of Canada Working Paper Nr. 6, February.

BIS, (2012), A Framework for Dealing with Domestically Important Banks.

International Association of Insurers, (2013), Global Systemically Important Insurers: Initial Assessment Methodology. For an overview of the challenges in the insurance sector see: J. Adams, (2014), Global Systemically Important Insurers: Issues, Policies and Challenges After Designation, Speech Bank of England, March 24, 2014. Also: H. Chen and T. Sun, (2017), Tail Risk Networks of Insurers Around the Globe: An Empirical Examination of Systemic Risk for G-SIIs v.s. Non G-SIIs, Fox School of Business Research Paper Nr. 17-038; S. Harrington, (2016), Systemic Risk and Regulation: The Misguided Case of Insurance SIFIs, Wharton School Working Paper, October. Also here a framework was developed for the effective resolution of SIIs: FSB, (2016), Developing Effective Resolution Strategies and Plans for Systemically Important Insurers, June 6.

FSB, (2014), Assessment Methodologies for Identifying Non-Bank Non-Insurer Global Systemically Important Financial Institutions, consultation document; 2nd Consultative Document by FSB on March 4, 2015. Following feedback on earlier released proposals, the new paper includes near-final methodologies for finance companies and market intermediaries (broker-dealers), a revised methodology for investment funds (including hedge funds), and a new proposed methodology for asset managers. The methodologies are expected to be finalized by the end of 2015, after which the FSB and IOSCO will begin work to develop any policy measures needed to address the risks posed by NBNI G-SIFIs, which are expected in the course of 2016. See also: FSB, (2015), Next Steps on the Assessment Methodologies for Non-Bank Non-Insurer Global Systemically Important Financial Institutions (NBNI G-SIFIs), press release, July 30. See also: BIS, (2017), Global systemically important banks - revised assessment framework - consultative document, March 30.

See M. Billio et al., (2015), An Entropy-Based Early Warning Indicator for Systemic Risk, University of Venice, Department of Economics Working Paper Nr. 09/WP/2015, May 7. The analysis is based on the cross-sectional distribution of marginal systemic risk measures such as Marginal Expected Shortfall, Delta CoVaR and network connectedness. Entropy indicators show promising forecast abilities to predict financial and banking crisis. The proposed early warning signals reveal to be effective in forecasting financial distress conditions. In line herewith see: S. Markose et al., (2017), Early Warning and Systemic Risk in Core Global banking: Balance Sheet Financial Network and Market Price-Based Methods, SSRN E-Journal, January. They conclude, and in line with Markose’s earlier work discussed, that ‘[t]he network based eigen-pair method simultaneously gives early warning of instability of the global banking system in terms of tipping points identified by regulatory capital thresholds and also the centrality measures for both systemically important and vulnerable banking systems.’ Also: I. Aldoraso et al., (2018), Early Warning Indicators of banking Crises: Expanding the Family, BIS Quarterly Report, April, pp. 29–45.

Most recent lists were published on their website in 2018 (www.​financialstabili​tyboard.​org)

See in detail: C. Siegert and M. Willison, (2015), Estimating the Extent of the ‘Too Big To Fail Problem- A Review of Existing Approaches, Bank of England, Financial Stability Paper Nr. 32, February; O. Couwenberg and S.J. Lubben, (2019), Not a Bank, Not a SIFI; Still Too Big to Fail, Emory Bankr. Dev. Journal Vol. 35, pp. 53 ff.

See: D. Lucas, (2014), Evaluating the Government as a Source of Systemic Risk, Journal of Financial Perspectives, Vol. 2, No. 3.

Or to be more precise a collection of loosely affiliated financial institutions including Fannie Mae, Freddie Mac, the Federal Home Loan Banks, deposit insurance, and the Pension Benefit Guarantee Corporation (PBOC).

That is based on Markose’s historical work on markets. Markose looks at markets as Complex Adaptive Systems (CAS); See: S.M. Markose, (2003), Computability and Evolutionary Complexity: Markets as Complex Adaptive Systems, Working Paper, September, mimeo.

See for the most recent version: S. M. Markose, (2013), Systemic Risk Analytics: A Data-Driven Multi-Agent Financial Network Approach, Journal of Banking Regulation, Vol.14, Issue 3/4, pp. 285–305, Special Issue on Regulatory Data and Systemic Risk Analytics.

S.M. Markose, (2012), Systemic Risk from Global Financial Derivatives: A Network Analysis of Contagion and its Mitigation with Super-Spreader Tax, IMF Working Paper, WP/12/282. Based on 2009 FDIC and individually collected firm-level data covering gross notional, gross positive (negative) fair value, and the netted derivatives assets and liabilities for 202 financial firms, which includes 20 SIFIs, the bilateral flows are empirically calibrated to reflect data-based constraints. This produces a tiered network with a distinct highly clustered central core of 12 SIFIs that account for 78% of all bilateral exposures and a large number of financial intermediaries on the periphery. The topology of the network results in the ‘Too-Interconnected-to-Fail’ (TITF) phenomenon in that the failure of any member of the central tier will bring down other members with the contagion coming to an abrupt end with the demise of the ‘super-spreaders’. Besides the fact that the OTC derivatives are not cleared and therefore prone to counterparty risk, there seems to be asymmetries in a lot of the OTC market in terms of discriminatory pricing. See in extenso: H. Hau et al., (2019), Discriminatory pricing of Over-the-Counter Derivatives, IMF Working Paper Nr. WP/19/100, May 7.

In the wake of Markose, Arsov et al. have developed a systemic financial stress index measuring the tail risk in the financial industry; see: I. Arsov, E. Canetti, L. Kodres and S. Mitra, (2013), ‘Near-Coincident’ Indicators of Systemic Stress, IMF Working Paper Series, WP/13/115. See also for an alternative stress index: R. Vermeulen et al., (2015), Financial Stress Indices and Financial Crises, DNB Working Paper Nr. 469, March. See for an alternative financial stress index: B. Vasicek et al., (2015), Leading Indicators of Financial Stress: New Evidence, DNB Working Paper Nr. 476, June.

S. Markose, S. Giansante, M. Gatkowski and A.R. Shanghaghi, (2010), Too Interconnected To Fail: Financial Contagion and Systemic Risk in Network Model of CDS and Other Credit Enhancement Obligations of US Banks, Original Essex University Working Paper Series, Nr. 683, which was later on updated; see: S. Markose, S. Giansante, and A. Shaghaghi (2012), Too Interconnected To Fail Financial Network of U.S. CDS Market: Topological Fragility and Systemic Risk, Journal of Economic Behavior and Organization, Vol. 83, Issue 3, August 2012, pp. 627–646. Regarding the relationship between CDS and systemic financial risk see: S. Giglio, (2016), Credit Default Swap Risk and Systemic Financial Risk, ESRB, Working Paper Series Nr. 15, June and M. D’Errico et al., (2016), How Does Risk Flow in the Credit Default Swap market?, ESRB Working Paper Series Nr. 33, December. Paddrik et al. analyzed counterparty exposure in the CDS market. In particular they looked at the impact of severe credit shocks on the demand for variation margin, which are the payments that counterparties make to offset price changes. Large and sudden demands for variation margin may exceed a firm’s ability to pay, leading some firms to delay or forego payments. These shortfalls can become amplified through the network of exposures. Also they refer to the role of CCPs in this matter. Typically seen as transmitting risk less than individual firms (firms that are large net sellers of CDS protection), they are positioned so that in case of large shortfalls it could lead to further shortfalls for their counterparties, amplifying the initial shock. See in detail: M. Paddrik et al., (2016), Contagion in the CDS Market, OFR Working Paper Nr. 16-12, December 1.

S. Markose, B. Oluwasegun and S. Giansante, (2012), Multi-Agent Financial Network (MAFN) Model of US Collateralized Debt Obligations (CDO): Regulatory Capital Arbitrage, Negative CDS Carry Trade and Systemic Risk Analysis, Chapter in Simulation in Computational Finance and Economics: Tools and Emerging Applications, Alexandrova-Kabadjova B., S. Martinez-Jaramillo, A. L. Garcia-Almanza, and E. Tsang, (eds.) IGI Global, August 2012. E. Gerding, (2016), The Dialectics of bank Capital: Regulation and Regulatory Capital Arbitrage, Washburn Law Journal, Vol. 55, pp. 357–384.

The FSB developed such a list in 2010: See FSB, (2010), Reducing the Moral Hazard by Systemically Important Financial Institutions. FSB Recommendations and Time Lines.

An eigen-pair is the mathematical pair of an eigenvector and its associated eigenvalue. The eigenvalue is a special set of scalars associated with a linear system of equations (i.e. a matrix equation), which in math is often used as a technique to measure the stability of a certain proposition. The eigenvector centrality (EVC) measures the nature and intensity of the relatedness of nodes in a network. It is applied in math or physics but also in social science. In this context it measures the nature and magnitude (what transactions, what volumes, what type of risks, how often these transactions change or expire) of the relationship of an FI with other FIs in the interconnected financial industry. Eigenvector centrality is one of the methods for computing the ‘centrality’, or approximate importance, of each node in a graph. The assumption is that each node’s centrality is the sum of the centrality values of the nodes that it is connected to. The nodes are drawn with a radius proportional to their centrality.

A. Haldane, (2009), Why Banks Failed the Stress Test, In Speech given at the Marcus-Evans Conference on Stress Testing, Bank of England (February). See regarding the decomposition of the effect of changing economic conditions, interest rates and balance sheet compositions on stress testing models: R. Busch et al., (2017), Deutsche Bundesbank Working Paper Nr. 7, Frankfurt am Main.

These tail risks seem to be very good indicators of bank distress. Early warning models including estimated tail dependencies consistently outperform bank-specific benchmark models without networks to predict systemic risk. See T.A. Peltonen et al., (2015), Network Linkages to Predict banking Distress, ECB Working Paper Nr. 1828, July. They use a multivariate extreme value theory to estimate equity-based tail-dependence networks, whose links proxy for the markets’ view of bank interconnectedness in case of elevated financial stress. See also: J. So and T.U, (2017), VAR and Stress Tests: The Impact of Fat-Tail Risk and Systemic Risk on Commercial Banks in Hong Kong and China, HKIMR Working Paper Nr. 14/2017; T. Breuer and M. Summer, (2018), Systematic Systemic Stres Tests, ONB Working Paper Nr. 225, December.

The parallel with the Pigovian application in environmental situations is clear. Also in that context an indicator signaling externalities with system-wide ecological ramifications is absent.

S. Choi et al., (2017), Financial Contagion in Networks: A Market Experiment, Working Paper, June 27, mimeo. They investigate how the network structure of financial linkages and uncertainty about the location of a shock affect the likelihood of contagion and the formation of prices. They conclude that ‘core-periphery’ networks (highly connected at the core and poorly connected at the periphery) are highly susceptible to contagion and generate fire sales of assets that exacerbate financial contagion beyond the mechanical role of network structure. In contrast, contagion is minimal on circle networks (in circle networks each participant is the sole creditor of one participant and the sole debtor of another participant) and market prices remain stable.

A. Haldane, (2009) Rethinking the Financial Network, Speech Delivered at the Financial Student Association, Amsterdam; J.D. Hamilton, (1989), A New Approach to the Economic Analysis of Non Stationary Time Series and Business Cycle, Econometrica, Vol. 57, Issue 2, pp. 357–384. See also regarding the relationship between financial contagion and spillover effects through both (in)direct channels: G. Peralta and R. Crisóstomos, (2016), Financial Contagion with Spillover Effects: A Multiplex Network Approach, ESRB Working Paper Series Nr. 32, December.

More recent analysis demonstrates that surcharges of 3.00 to 8.25% would be justified and that they would have to be even higher for GSI-Bs that rely on short-term funding. See in detail: W. Passmore and A.H. von Haffen, (2019), Are Basel’s Capital Surcharges for Global Systemically Important Banks Too Small?, International Journal of Central Banking, March, pp. 107–156.

Section 7004 of the ‘Tax Reform Act of 2014’ would impose a quarterly excise tax of 0.035% on systemically important financial institutions (SIFIs). The excise tax would be applied to a SIFI’s total consolidated assets in excess of $500 billion. For the purposes of the legislation, a SIFI would be any bank holding company with at least $50 billion in total consolidated assets, or any non-bank financial institution designated for SIFI treatment by the Financial Stability Oversight Council and subject to oversight by the Federal Reserve Board. The $500 billion threshold would be indexed for increases in the gross domestic product (GDP) after calendar year 2015.

See: Committee on Ways and Means, (2014), Tax Reform Act 2014, Chairman Dave Camp, Section-by-Section Summary, pp. 180–181.

The Greens, (2014), Implicit Subsidies in the EU Banking Sector, Intermediate Reporting (January). Credit Subsidies do however seem to function well as an alternative macroprudential tool. See: I. Correia et al., (2016), Credit Subsidies, ECB Working Paper Series Nr. 1877, January. Gudmundsson uses a jump diffusion option-pricing approach to provide estimates of implicit subsidies gained by these banks due to the expectation of protection to creditors provided by governments. He concludes that ‘[w]hile these subsidies have declined in the post-crisis era as volatility has declined and capital levels have increased, they remain non-trivial. Even conservative parameterizations of default and loss probabilities lead to macroeconomically significant figures.’ See: T. Gudmundsson et al., (2016), Whose Credit Line is it Anyway: An Update on Banks’ Implicit Subsidies, IMF Working Paper Nr. 16/224, November.

See also: S. Schich and S. Lindh, (2012), Implicit Guarantees for Bank Debt: Where Do We Stand?, OECD Journal, Financial Market Trends, Vol. 2012, Issue 1, pp. 1–22.

See for a full overview of all models: S. Markose and S. Giansante, (2014), Pigou Tax of Systemically Important Financial Intermediaries (SIFIs) In Financial Networks: An Empirical Application of Systemic Risk Monitoring and Governance, Working Paper. See also: G. Demange, (2015), Contagion in Financial Networks: A Threat Index, CESifo Working Paper Nr. 5307, April. This paper introduces ‘a measure of the spill-over effects that a bank generates when it defaults. The measure is based on an explicit criterion, the aggregate debt repayments, and is bank’s specific, affected by the bank’s characteristics and links to other banks. Such measure can be useful to a regulator to determine in which banks cash should be injected during a default episode or to evaluate the impact of raising capital before the occurrence of default. The approach applies more generally to a system of entities that are linked through financial claims. Interbank claims are a concern to regulators as they might facilitate the dissemination of defaults and generate spill-over effects’.

H. Minski, (1982), The Financial-Instability Hypothesis: Capitalist Processes and the Behavior of the Economy, in Ch. P. Kindleberger and J-P. Laffargue (eds.), Financial Crises: Theory, History and Policy, Cambridge University Press, Cambridge, pp. 13–38. See for a very good overview of Minsky’s work and thinking on financialization: C.A. Whalen, (2017), Understanding Financialization: Standing on the Shoulders of Minsky, Levy Economics Institute of Bard College, Working Paper Nr. 892, June.

See for a comprehensive analysis of financial stability risk in shadow banking systems: K. van der Veer et al., (2015), Shedding a Clearer Light on Financial Stability Risks in the Shadow Banking System, DNB Occasional Studies Vol. 13–7.

Markose et al., (2014), Ibid. p. 5.

See also: G. Leyngar et al., (2017), Towards a Financial Statement Based Approach to Modeling Systemic Risk in Insurance and Banking, Columbia Business School Research Paper Nr. 17-77. They highlight that one of the important limitations of the SRISK measure of systemic risk is its reliance on stock market data without much validation against the institutions’ fundamentals based on its financial statements. They therefore propose a financial-statement-based approach to estimating the vulnerability of an institution to a systemic event (labeled CRISK). Applying their model to three different businesses, they conclude that under circumstances SRISK is likely to overstate (misstate) capital requirements and overstate capital requirements for banks heavily reliant on FDIC insured deposits. They suggested methodology could be used as a first-line filter to identify systemically important institutions.

Recently Battiston et al. produced a ‘novel, very interesting three-level monitoring framework for systemic risk. The modular structure of the framework allows to accommodate for a variety of shock scenarios, methods to estimate interbank exposures and mechanisms of distress propagation. The main features are as follows. First, the framework allows to estimate and disentangle not only first-round effects (i.e. shock on external assets) and second-round effects (i.e. distress induced in the interbank network), but also third-round effects induced by possible fire sales. Second, it allows to monitor at the same time the impact of shocks on individual or groups of financial institutions as well as their vulnerability to shocks on counterparties or certain asset classes. Third, it includes estimates for loss distributions, thus combining network effects with familiar risk measures such as VaR and CVaR. Fourth, in order to perform robustness analyses and cope with incomplete data, the framework features a module for the generation of sets of networks of inter- bank exposures that are coherent with the total lending and borrowing of each bank. They find that second-round and third-round effects dominate first-round effects, therefore suggesting that most current stress-test frameworks might lead to a severe underestimation of systemic risk’; see: S. Battiston et al., (2015), Leveraging the Network: A Stress-Test Framework based on DebtRank, Working Paper, March 3, Mimeo; M. Paddrik et al., (2016), Bank Networks and Systemic Risk: Evidence from the National Banking Acts, OFR Working Paper Nr. 16-13, December 6; E. Frohm and V. Gunnella, (2017), Sectoral Interlinkages in Global Value Chains, ECB Working Paper Nr. 2064, May. Sectoral spillovers are both statistically significant and of economic importance.

Financial Intermediation in a ‘global’ context might be added. The interbanking market is by definition global and the propagation shocks are material running through that channel. International transmission of a crisis through the balance sheet of global banks is directly connected to the international asset market and its pricing mechanism. See in detail: V. Nuguer, (2016), Financial Intermediation in a Global Environment, IJCB, Vol. 12, Nr. 3, pp. 291–343.

‘How, and to what extent, does an interconnected financial system endogenously amplify external shocks’ is the question that Visentin asked themselves. They conclude that regardless of the shock distribution and the network topology, precise ordering relationships on the level of aggregate systemic losses hold among models. They argue that ‘the extent of contagion crucially depends on the amount of information that each model assumes to be available to market players.’ Under no uncertainty about the network structure and values of external assets, the lowest level of contagion is delivered. That phenomenon is known as ‘loss conservation’ which implies that ‘aggregate losses after contagion are equal to the losses incurred by those institutions initially hit by a shock. This property implies that many contagion analyses rule out by construction any loss amplification, treating de facto an interconnected system as a single aggregate entity, where losses are simply mutualised.’ On the other hand ‘non -conservative losses’ get compounded through the network. The endgame in terms of policy implication is that by reducing the levels of uncertainty in times of distress, we would be able to move toward lower levels of contagion; see in detail also for the set of models analyzed: G. Visentin et al., (2016), Rethinking Financial Contagion, University of Zurich Working Paper, mimeo, August 28. Also: D.F. Ahelegbey et al., (2019), Tree Networks to Assess Financial Contagion, MPRA Paper Nr. 92632, March 29.

See Markose et al., (2014), Section 3, pp. 20–23.

C.H. Furfine, (2003), Interbank Exposures: Quantifying the Risk of Contagion, Journal of Money, Credit and Banking, Vol. 35, Issue 1, pp. 111–28. Furfine developed the framework for quantifying contagion risk and the domino effect it creates. See Markose et al., (2014), pp. 24–27. Also interesting in a wider context: M. Carlson and D.C. Wheelock, (2016), Did the Founding of the Federal Reserve Affect the Vulnerability of the Interbank System to Contagion, BIS Working Paper Nr. 598, December. They find that the interbank system became more resilient to solvency shocks, but less resilient to liquidity shocks, as banks sharply reduced their liquidity after the Fed’s founding.

Supra 11.6.2.3.

Lehman Brothers capital ratios did meet all the Basel criteria on the last working days before it failed on Sunday, given the lack of government support or backstop.

L. Blattner et al., (2019), When Losses Turn into Loans: the Cost of Undercapitalized Banks, ECB Working Paper Nr. 2228, January 28.

See for an extensive analysis of systemic risk in the insurance sector: EIOPA, (2017), Systemic Risk and Macroprudential Policy in Insurance, European Insurance and Occupational Pension Authority Report.

V.V. Acharya, L. H. Pedersen, T. Philippon and M. Richardson, (2009), Measuring Systemic Risk, Working Paper, New York University Stern School of Business; V.V. Acharya, L. H. Pedersen, T. Philippon and M. Richardson (2009) Regulating Systemic Risk, Chapter 13 in Restoring Financial Stability: How to Repair a Failed System, V. V. Acharya and M. Richardson (eds.), New York University Stern School of Business, John Wiley and Sons, Hoboken; V.V. Acharya, L.H. Pederson, T. Philippon and M. Richardson, (2010), A Tax on Systemic Risk, Working Paper. V.V. Acharya, (2009), A Theory of Systemic Risk and Design of Prudential Bank Regulation, Journal of Financial Stability Elsevier, Vol. 5, Issue 3, pp. 224–255. And more recently: S. Benoit et al., (2015), Where the Risks Lie: A Survey on Systemic Risk, University of Orléons Working Paper, Mimeo; J.H. Dalhuizen, (2015), The management of Systemic Risk from a Legal Perspective, Working Paper, January, Mimeo; Z. Feinstein et al., (2015), Measuring of Systemic Risk, Working Paper, March, Mimeo. G. Löffler and P. Raupach, (2014), Pitfalls in the Use of Systemic Risk Measures, Working Paper, October 2. They identified several cases in which a change in a bank’s systematic risk, idiosyncratic risk, size or contagiousness increases the risk of the system but lowers the measured systemic risk contribution of the bank. In such cases, assessments based on estimated systemic risk contributions could produce false interpretations and incentives. See also: V. de Bruyckere, (2015), Systemic Risk Rankings and Network Centrality in the European Banking Sector, ECB Working Paper Nr. 1848, September and C. Siebenbrunner, (2017), Clearing Algorithms and Network Centrality, Working Paper, April 28, mimeo. Regarding the informational value of systemic risk rankings see: F. Nucera et al., (2016), The Information in Systemic Risk Rankings, ECB Working Paper Series Nr. 1875, January. Das proposes a novel framework for network-based systemic risk measurement and management. A new systemic risk score is defined that depends on the level of individual risk at each financial institution and the interconnectedness across institutions, and is generally applicable irrespective of how interconnectedness is defined. This risk metric is decomposable into risk contributions from each entity, forming a basis for taxing each entity appropriately. The paper further develops other risk measures such as system fragility and entity criticality. An assessment using a measure of spillover risk is obtained to determine the scale of externalities that one bank might impose on the system; the metric is robust to this cross risk and does not induce predatory spillovers. The analysis shows that splitting up too-big-to-fail banks from the system does not lower systemic risk; see: S.R. Das, (2015), Matrix Metrics: Network-Based Systemic Risk Scoring, Leavy School of Business Working Paper, Santa Clara University, November 15.

V.V. Acharya, C. Brownelees, R. Engle, F. Farazmand and M. Richardson, (2010), Measuring Systemic Risk, Chapter 4 in Regulating Wall Street: The Dodd-Frank Act and the New Architecture of Global Finance, V.V. Acharya, T. Cooley, M. Richardson and I. Walter (eds.), John Wiley & Sons, Hoboken, pp. 199–232. In a European context regarding this model see: V.V. Acharya and S. Steffen, (2012), Analyzing Systemic Risk in the Banking Sector, Working Paper. See also: B. Weder di Mauro, (2010), Taxing Systemic Risk: Proposal for a Systemic Risk Levy and a Systemic Risk Fund, University of Mainz Working Paper. See for a ranking of European financial institution based on the MES: A. Derbali, (2017), Systemic Risk of European Financial Institutions: Estimation and Ranking by the Marginal Expected Shortfall, University of Sousse Working Paper, mimeo, November 28. He concludes that the systemic risk supported by European banks is very high. Moreover, the contribution of financial institutions in the risk of their system is very important as a result of the high correlation between institution returns and market returns. He further produced a measurement based on the MES for Chinese, Greek and US financial institutions. Avikran did the same for Japanese banks: N.K. Avkiran, (2017), Measuring the Systemic Risk of Regional Banks in Japan, University of Queensland Business School Working Paper, November 20, mimeo. For Russian banks see: A. Karminsky and M. Stolbov, (2016), Assessing the Link between Financial Soundness and Systemic Risk for Key Russian Banks, Journal of Corporate Finance Research, Vol. 10, Nr. 1, pp. 77–87; C. Buch et al., (2017), Drivers of Systemic Risk: Do National and European Perspectives Differ?, Bundesbank Discussion Paper Nr. 09/2017; T. Beck et al., (2017), Bank Sectoral Concentration and (Systemic) Risk: Evidence from a Worldwide Sample of Banks, CEPR Discussion Paper Nr. DP12009, June. Li and Saiz measure systemic risk in the network of financial market infrastructures (FMIs) as the probability that two or more FMIs have a large credit risk exposure to the same FMI participant. They not only observe large differences between the contributions to systemic risk across participants, they also find that when participants are in financial distress, they tend to create large credit exposures in two or more FMIs. In detail: F. Li and H.P. Saiz, (2016), Measuring Systemic Risk across Financial market Infrastructures, Bank of Canada Working Paper Nr. 10, March.

C. Brownlees and R. Engle, (2011), Volatility, Correlation and Tails for Systemic Risk Measurement, Working Paper Series, Department of Finance, NYU advanced the model. Idier et al. however conclude that standard balance-sheet metrics like the tier one solvency ratio are better able than the MES to predict equity losses conditionally to a true crisis (using the 2008 datasets); See: J. Idier, G. Lamé, and J.-S. Méssonier, (2013), How Useful is the Marginal Expected Shortfall for the Measurement of Systemic Exposure, A Practical Assessment, ECB Working Paper Series, Nr. 1546.

I will limit myself to referring to other models developed insofar as they are not mentioned in the main text. They have contributed one way or the other to the understanding of the occurrence of systemic risk but have, as insight has progressed, become known for adding particular features or a specific understanding rather than a continued validation of their overall model. They include:

See for a comparison of the different models suggested: John Sedunov III, (2013), What is the Systemic Risk Exposure of FIs?, Midwest Finance Association 2013 Annual Meeting Paper. See also J.-P. Fouque and J.A. Angsam, (2013), Handbook on Systemic Risk, Cambridge University Press, Cambridge; and S. Giglio et al. (2016), Systemic Risk and the Macroeconomy: An Empirical Evaluation, Journal of Financial Economics, Vol. 119, Nr.1, pp. 457–471.

J.P. Solorzano-Margain, S. Martinez-Jaramillo and F. Lopez-Gallo, (2013), Financial Contagion: Extending the Exposures Network of the Mexican Financial System, Computational Management Science, Vol. 10, Issue 2–3, pp. 125–155.

A similar non-EVC model was recently used to visualize the Dutch Overnight Money Market; See: R. Heijmans, R. Heuver, C. Levallois and I. van Lelyveld, (2014), Dynamic Visualisation of Large Transaction Networks: The Daily Dutch Overnight Money Market, DNB Working Paper Series, Nr. 418.

S. Markose, (2013), Systemic Risk Analytics: A Data Driven Multi-Agent Financial Network (MAFN) Approach, Submitted Special Issue Journal of Banking Regulation: Future of Regulatory Data and Systemic Risk Analytics, Bank of England Workshop January 17–18, 2013.

See, for instance, for a regional study: N. Zhang and X. Zhao, (2018), Measuring Global Flow of Funds: A Case Study on China, Japan and the United States, Paper prepared for the 35th IARIW General Conference Copenhagen, Denmark, August 20–25, mimeo.

Markose et al., (2014), Ibid. p. 29.

See for an attempt of an integrated approach in this matter: A. Serguaiva, (2013), Systemic Risk Identification, Modelling, Analysis, and Monitoring: An Integrated Approach, University College London, Working Paper.

See in detail: M. Bijlsma, J. Klomp and S. Duineveld (CPB), (2010), Systemic Risk in the Financial Sector: A Review and Synthesis, CBP Documents Nr. 210, pp. 53–70. For the impact on the most adequate form of supervision see: T. Beck and W. Wagner, (2013), Supranational Regulation: How Much and From Whom?, CEPR Discussion Paper Nr. DP9546.

See S.G. Cechetti, (2014), Systemic Risk and the Solvency-Liquidity Nexus of Banks, Brandeis International Business School Presentation, April 25 and T. Adrian, (2015), Discussion of ‘Systemic Risk and the Solvency-Liquidity Nexus of Banks’, Federal Reserve bank of NY Staff Reports, Nr. 722, April.

NBB, (2015), Financial Stability Review, June, p. 12. Ari et al., (2016), conclude that ‘the shadow banking sector has a tendency to expand to a size where it ferments systemic risk. When the shadow banking sector is relatively small, secondary market purchases by traditional banks cushion the liquidation value of shadow banks and help keep their borrowing costs low. In periods of stability, this culminates in the expansion of the shadow banking sector until it reaches a size where purchases by traditional banks cannot prevent a re-sale in the event of a fundamental run. The liquidation of shadow banks then leaves traditional banks susceptible to liquidity runs. This increases the borrowing costs of traditional banks and weakens market discipline on them, engendering greater risk-taking and a rise in insolvency risk’ (pp. 28–29). See in detail: A. Anil et al., (2016), When Shadows Grow Longer: Shadow banking with Endogeneous Entry, ECB Working Paper Series Nr. 1943, August. Particular is the natural flow of events and the destabilizing consequences. They propose a tax on shadow banks with the purpose of reducing the size of the shadow banking sector to a level compatible with financial stability. They refer to their proposal as a Pigouvian tax since the adoption of a shadow banking strategy imposes a negative externality on the remainder of the financial sector through its contribution to re-sales.

An interesting question is whether higher capital banks really decrease systemic risk. Bostandzic et al. Looked into this. Their results are sobering as they find an individual bank’s contribution to systemic fragility to decrease only marginally after the introduction of higher capital requirements. Moreover, this decrease in systemic risk is transitory and vanishes two years after the EBA capital exercise: see in detail: D. Bostandzic et al., (2017), Do Higher Bank Capital Requirements Really Decrease Systemic Risk?, Leeds University Business School Working Paper Nr. 18-02, mimeo.

T. Gehrig et al., (2018), Did the Basel Process of capital Regulation Enhance the Resilience of European Banks, Bank of Finland Research Discussion Paper Nr. 16, Helsinki. To reach some interesting conclusions: they find that the exposure to systemic risk as measured by SRISK has been steeply rising for the highest quintile, moderately rising for the second quintile and remaining roughly stationary for the remaining three quintiles of listed European banks. This observation suggests that the Basel process has succeeded in containing systemic risk for the majority of European banks but not for the largest and most risky institutions. They find compelling evidence that the increase in exposure to systemic risk (SRISK) is intimately tied to the implementation of internal models for determining credit risk as well as market risk. Based on this evidence, the subprime crisis found especially the largest and more systemic banks ill-prepared and lacking resiliency. They also point out that the EU banking union has not restored aggregate resiliency to pre-crises levels.

See for an optimal macroprudential policy, similar to a countercyclical capital requirement that can eliminate systemic risk: S. Laséen et al., (2017), Systemic Risk: A New Trade-Off for Monetary Policy?, Sveriges Riksbank Working Paper Series Nr. 341, August. See for an application of countercyclical capital buffers: V. Flamini et al., (2019), Credit Cycle and Capital Buffers in Central America, Panama, and the Dominican Republic, IMF Working Paper Nr. WP/19/39, February. What makes the case study interesting is the fact that emerging countries are more exposed to sudden external shocks and reversal in capital flows. Monitoring the credit cycle therefore is crucial to avoid excess aggregate credit growth and the build-up of imbalances in the system that could turn systemic in nature. The ‘credit gap’ which they use as an early warning feature refers to the credit-to-GDP gap (i.e., the difference between private sector credit-to-GDP ratio and its estimated trend). One ultimate needs a model to assess whether an observed surge in credit is excessive (or not) and/or requires a response. Although the credit-to-GDP gap can be used as an anchor to assess imbalance and systemic risk ahead, a more comprehensive assessment is often required. Under those circumstances the countercyclical capital buffers introduced by Basel III tend to work their magic. For an EU application see: A. Samarina et al., (2017), Credit Cycle Coherence in the Eurozone: Was There a Euro Effect?, Journal of International Money and Finance, Vol. 77, pp. 77–98. There are however some conceptual issues in the calibration of the countercyclical capital buffer. Under Basel III, the main indicator for buffer decisions is the credit-to-GDP gap. It however doesn’t work best in terms of covering bank loan losses that go beyond what could be expected from economic downturns. Instead, in the case of countries with short financial cycles and/or low financial deepening such as transition and developing economies, the Basel gap is shown to work best when computed with a low, smoothing factor and adjusted for the degree of financial deepening. Also finding an appropriate trade-off between stability of the buffer size and cost efficiency seems problematic. See in detail: T. Wezel, (2019), Conceptual Issues in Calibrating the Basel III Countercyclical Capital Buffer, IMF Working Paper Nr. WP/19/86, May. Also BCBS, (2019), Towards a Sectoral Application of the Countercyclical Capital Buffer, BCBS Working Paper Nr. 36, April. To be read in conjunction with: BCBS, (2018), Towards a Sectoral Application of the Countercyclical Capital Buffer: A Literature Review, BCBS Working Paper Nr. 32, March, via bis.​org. Yet, countercyclical capital buffers, both broad-based and sectoral, remain largely untested and more work is needed to assess their ability to achieve the different objectives attributed to them. Furthermore, a sectoral application of the CCyB entails several challenges with respect to the design of the instrument and its interactions with the Basel III CCyB and other (targeted) instruments. Also: S. Ferrari and P. R. Kaltwasser, (2018), Sectoral Credit Cycles and Systemic Risk in the United States, Working Paper, February 28, mimeo; C. Couaillier et al., (2019), Activation of Countercyclical Capital Buffers in Europe: Initial Experiences, Banque de France Bulletin Nr. 222, May 17. Also F.M. e Castro, (2019), A Quantitative Analysis of Countercyclical Capital Buffers, FRB of St. Louis Nr. 8C, June. First, he shows that raising capital buffers during leverage expansions can reduce the frequency of crises by more than half. Second, he shows that lowering capital buffers during a panic can moderate the intensity of the resulting crisis.

There is a trade-off between additional loss-absorbing capacity and potentially higher bank risk-taking associated with the introduction of the Basel III Leverage Ratio. Smith et al. document that banks can feel incentivized especially those that are bound by the leverage ratio to increase their risk-taking. This increase in risk-taking, however, should be more than outweighed by the benefits of higher capital and therefore increased loss-absorbing capacity, thereby leading to more stable banks. See in detail: J.A. Smith et al., (2017), The Leverage Ratio, Risk-Taking and Bank Stability, ECB Working Paper Nr. 2079 June. See for a recent revision to the ratio: BIS, (2019), Leverage Ratio Treatment of Client Cleared Derivatives, June 26, via bis.​org

Changes in maximum LTV ratios have modest and imprecisely estimated effects on output and inflation. The output effect is more pronounced in emerging market economies than in advanced economies, and mainly driven by tightening LTV limits. Tightening LTV limits reduces housing credit and house prices. Our results indicate that for central banks, macroprudential measures may serve as a complementary policy tool. See in detail: B. Richter et al., (2018), The Macroeconomic Effects of Macroprudential Policy, BIS Working Paper Nr. 740, August 27, via bis.​org. J. Kelly et al., (2019), Pockets of Risk in European Housing Markets: Then and Now, ESRB Working Paper Nr. 7, February. Kelly et al. documented higher LTV/LTI ratios prior to the crisis, but despite the long period of historically low interest rates and substantial house price increases in some countries, they do not find similar credit easing as before the crisis. In times of high LTV ratios, effects of monetary policy on real mortgage credit, real house prices, real consumption of durables and non-durables, and, ultimately, on real GDP are more pronounced. In detail: T. Franz, (2019), Monetary Policy, Housing, and Collateral Constraints, Deutsche Bundesbank Discussion Paper Nr. 2, January 29; H. Balfoussia et al., (2018), Loan-to-Value Ratio Limits: An Exploration for Greece, Bank of Greece Working Paper Nr. 248, July; G. Cokayne, (2019), The Effect of Macro-Prudential Policies on House Price Cycles in an Agent-Based Model of the Danish Housing Market, DNB Working Paper Nr. 138, May 24.

A simple cap on the LTV ratio is effective in smoothing the credit cycles against the background of systemic crises, which are rare and non-linear events. See in detail: E. Gerba and D. Żochowski, (2017), Knightian Uncertainty and Credit Cycles, ECB Working Paper Nr. 2068, May, p. 47; S. Pool, (2018), Mortgage Debt and Shadow Banks, DNB Working Paper Nr. 588, March, 6–7. Playing around with the LTV ratio as is often done by regulators and supervisors has less effect that often thought. In that sense the initial LTV level appears to matter more than the adjustments made later on. When LTV limits are already tight, the effects of additional tightening on credit is dampened. See for more numerical analysis on LTV-ratio tampering and its effect on other economic factors: Z. Alam et al., (2019), Digging Deeper—Evidence on the Effects of Macroprudential Policies from a New Database, IMF Working Paper Nr. WP/19/66, March, pp. 3–4. For an interesting way of constructing the LTV limit see appendix 2 (p. 27). See also more in general: O. Akinci, and J. Olmstead-Rumsey, (2018), How Effective are Macroprudential Policies? An Empirical Investigation, Journal of Financial Intermediation, Vol. 33, pp. 33–57; A. Alter, et al., (2018), Understanding the Macro-Financial Effects of Household Debt: A Global Perspective, IMF Working Paper, WP/18/76; L. Brandao, et al., (2019), Toward a Cost-Benefit Analysis of Macroprudential and Monetary Policies, forthcoming; G. Galati, and R. Moessner, (2018), What Do We Know About the Effects of Macroprudential Policy? Economica, Vol. 85: pp. 735–770; T. Poghosyan, (2019), How Effective is Macroprudential Policy? Evidence from Lending Restriction Measures in EU Countries, IMF Working Paper Nr. WP/19/45, March.

It is not only the linkages within the banking sector but also between the banking sector and the shadow banking sector that create the interconnectedness which fuels that contagion channel during runs and market distress. An add-on complicating factor (e.g. in the EU) is when shadow banking entities are located in different jurisdictions (within the EU and outside, e.g. the EU where the exposure of EU banks during the Great Recession was 27% of total exposures). The expected diversification at the single bank level has been observed in terms of exposure (which in itself is rather low) to shadow banking entities but creates in an EU block a high overlap across different types of shadow banking entities. See for further analysis: J. Abad et al., (2017), Mapping the Interconnectedness between EU Banks and Shadow Banking Entities, ESRB Working Paper Nr. 40, March, pp. 9 ff.

To be precise it is both Basel III and IV that contributed to that end result. Since Basel III was rolled out (one can see in the December 2017 publication by the BCBS of the final regulatory standards in its post-crisis Basel III reforms a kind of culmination point of the Basel III maturity process; see bis.​org), the Basel Committee on Banking Supervision (BCBS) has been reviewing risk-measurement approaches internationally and among banks. One outcome of this review was the new standardized measurement approach (SMA) for operational risk, which was proposed in 2016. The committee also began a discussion on aggregated internal-rating model floors, concerned about the wide variation in the levels of risk-weighted assets (RWA) issuing from banks’ internal models. The committee finalized standards for minimum capital requirements for market risk—the fundamental review of the trading book (FRTB)—in January 2016. All post-Basel III recommendations and changes are collectively referred to as Basel IV. In the period 2017–2019 many of the technical revisions have been processed, but (country-specific) implementation is somewhat opaque and sketchy in terms of timeline. The idea of Basel IV is not to ‘increase the total regulatory capital requirements’ although the impact can be material for individual banks. McKinsey did some digging and concluded that the impact of Basel IV goes further and that the impact of Basel IV will be significant throughout the banking industry. See: S. Koch et al. (2017), “Basel ‘IV’: What’s next for banks?”, McKinsey.​com and S. Koch et al., (2018), Bringing Basel IV into Focus, April, McKinsey.​com

C.W. Calomiris and S.H. Haber, (2013), Fragile By Design. The Political Origin of Banking Crisis and Scarce Credit, Princeton University Press, Princeton.

See also: R. Nyman et al., (2018), News and Narratives in Financial Systems: Exploiting Big Data for Systemic Risk Assessment, Bank of England Working Paper, Nr. 704, January; S.R. Baker, (2016), Measuring Economic Policy Uncertainty, Quarterly Journal of Economics, Vol. 131, Issue 4, pp. 1593–1636; S.A. Sharpe, et al., (2017), What’s the Story? A New Perspective on the Value of Economic Forecasts. Finance and Economics Discussion Series, Nr. 2017-107. Board of Governors of the Federal Reserve System, Washington. On multiple occasions it was argued that the reflection on forecasts has not been sufficiently profound to trigger meaningful changes to the models, despite the fact that continuous proof is delivered that even the sophisticated (growth) forecasting models hardly do a better job than the models simply based on historical global sample average (constant) growth rate. There is too much weight placed on observable predictors and underestimating the forces of mean reversion. There are many large noise components, especially in long-term forecasts. It raises the question about ‘the usefulness of judgment-based medium and long-run forecasts for policy analysis (due to poor predictive performance, poor predictor of future developments, and underestimating the uncertainty in the forecasts), including for debt sustainability assessments’. See in detail: K.-P. Hellwig, (2018), Overfitting in Judgment-based Economic Forecasts: The Case of IMF Growth Projections, IMF Working Paper Nr. WP/18/260, November. He blames ‘overfitting’ for the lack of accuracy in the forecasting models. Hellwig explains: ‘[o]verfitting occurs when forecasters attempt to construct stories about the future based on past experience, not taking into account that this experience is limited. Whenever this occurs, forecasters behave like statistical models that are estimated on small non-representative samples. They pay too much attention to details that explain the data well in a limited sample but turn out to be less informative in a larger sample. As a result, overfitted models (and human forecasters) respond to noise rather than relevant information. (p. 2)’ Also: P. Beaudry and T. Willems, (2018), On the Macroeconomic Consequences of Over-Optimism, IMF Working Paper Nr. WP/18/122; P. Bordalo et al., (2018), Overreaction in Macro-Economic Expectations, Working Paper, March, mimeo; T. S Eicher, et al., (2018), Forecasts in Times of Crises, IMF Working Paper Nr. WP/18/48; P. Chatterjee and S. Nowak, (2016), Forecast Errors and Uncertainty Shocks, IMF Working Paper Nr. WP/16/228. Can Machine learning help in this context? See: J.-K. Jung, et al., (2018), An Algorithmic Crystal Ball: Forecasts Based on Machine Learning, IMF Working Paper Nr. WP/18/230, November and S. Mullainathan and J. Spiess, (2017), Machine Learning: an Applied Econometric Approach, Journal of Economic Perspectives, Vol. 31, Issue 2, pp. 87–106.

See BIS, (2011), Basel III: A Global Regulatory Framework for More Resilient Banks and Banking Systems.

Contingent Convertible (CoCo) bonds and other products with refined covenants are put in place to serve that purpose. See, for instance, S. Advjiev et al., (2017), CoCo Issuance and bank Fragility, BIS Working Paper Nr. 67, November 22, via bis.​org. Contingent convertible capital securities (CoCos) are hybrid capital securities that absorb losses when the capital of the issuing bank falls below a certain level. CoCos can absorb losses either by converting into common equity or by suffering a principal write-down. They have a ‘trigger’ which can be either mechanical (i.e. defined numerically in terms of a specific capital ratio) or discretionary (i.e. subject to supervisory judgment). CoCos have become an important part of the post-crisis regulatory framework. However, up until now there has been little evidence on how they work in practice. Advjiev et al. draw four major conclusions: First, larger and better-capitalized banks are more likely to issue CoCos. Second, issuing CoCos causes the issuers’ CDS spreads to decline. This indicates that CoCos reduce banks’ credit risk and lower their funding costs. This is especially true for CoCos that convert into equity or have mechanical triggers. Third, CoCos with only discretionary triggers do not have a significant impact on CDS spreads. Fourth, CoCo issues do not affect stock prices, except for principal write-down CoCos with a high trigger level, which have a positive effect. Also: N. Martynova and E. Perotti, (2018), Convertible Bonds and Bank Risk-Taking, Deutsche Bundesbank Discussion Paper Nr. 27, July 31; P. Bologna et al., (2018), Contagion in the CoCo Market? A Case Study of Two Stress Events, Bank of Italy Working Paper Nr. 1201, November 6.

In October 2016 the BCBS produced the final TLAC (Total Loss-Absorbing Capacity) Standards. See: BCBS, (2016), TLAC Holdings, October. These standards were introduced as an amendment to the Basel III standards. The document predominantly describes the criteria instruments need to fulfill in order to qualify as TLAC. In general, TLAC-eligible instruments must be subordinated to a list of excluded liabilities (e.g. insured deposits). Subordination may be embedded in contractual terms, prescribed in statute or achieved by being issued by a resolution entity that does not have any excluded liabilities that rank pari passu or junior to TLAC-eligible instruments (structural subordination). There are limited exemptions to the subordination requirements (p. 2).

See for a recent update of the different models currently in vogue: A. Lucas, B. Schwaab and X. Zhang, (2013), Measuring Credit Risk in a Large Banking System: Econometric Modeling and Empirics, Presentation Cleveland FED, May 30–31 also as paper: Tinbergen Institute Discussion Paper 13-063/IV/DSF56.

See BIS, (2011), Ibid. supra.

See: S. Krieger, (2011), Shadow Maturity Transformation and Systemic Risk, FED of New York Presentation, March 6.

At least that is the conventional wisdom. This holds that banks benefit from a steep yield curve because they intermediate funds across maturities by borrowing ‘short’ and lending ‘long’. However, a steepening of the yield curve caused by rising long-term interest rates will also result in immediate capital losses on longer-term assets, which may offset part of any benefits of higher net interest margins. The share prices of banks that engage more heavily in maturity transformation have a significantly less negative reaction to an unanticipated steepening of the yield curve, a result that partially confirms the conventional wisdom that banks benefit from a steeper yield curve due to their role as maturity transformers. More recently, it was also demonstrated that bank stocks respond significantly to exogenous fluctuations in interest rates induced by monetary policy announcements. Bank stock prices decline substantially following an unanticipated increase in the level of interest rates or a steepening of the yield curve. A large maturity gap, however, significantly attenuates the negative reaction of returns to a slope surprise, a result consistent with the role of banks as maturity transformers. The share prices of banks that rely heavily on core deposits decline more in response to policy-induced interest rate surprises, a reaction that primarily reflects ensuing deposit disintermediation; See: W.B. English, S.J. Van den Heuvel and E. Zakrajšek, (2014), Interest Rate Risk and Bank Equity Valuations, University of Pennsylvania (Wharton), Working Paper.

Together with the interbanking market, the asset-backed commercial paper market (ABCP) and the tri-party repo market. The latter two categories are part of the shadow banking market (Sect. 11.7).

It also explains most of the suggested changes regarding MMFs, Proposal for a Regulation of the European Parliament and of the Council on Money Market Funds (Com/2013/0615 final - 2013/0306 (COD) of September 3, 2013.

See M.M. Andreasen, M. Ferman and P. Zabczyk, (2012), The Business Cycle Implications of Bank’s Maturity Transformation, ECB Working Paper Series, Nr. 1489.

See in detail: O. Entrop, C. Memmel, B. Ruprecht and M. Wilkens, (2012), Determinants of Bank Interest Margins: Impact of Maturity Transformation, Deutsche Bundesbank Discussion Paper Nr. 17/2012; C. Memmel, (2010), Bank’s Exposure to Interest Rate Risk, Their Earnings from Term Transformation, and the Dynamics of the Term Structure, Deutsche Bundesbank, Banking and Financial Studies Series, Nr. 7/2010; C. Meppel, (2017), Why Do Banks Bear Interest Rate Risk, Deutsche Bundesbank Discussion Paper Nr. 35, Frankfurt am Main. Credit and interest rate risk cannot be fully separated when granting loans. But also long optimization horizons are a part of the banks and the hedging of earning risks arising from a decline of the interest rate level are reasons for bearing interest rate risk by FIs. Also: R. Busch, and C. Memmel, (2017), Banks’ Net Interest Margin and the Level of Interest Rates. Credit and Capital Markets, Vol. 50, Issue 3, pp. 363–392; S. Claessens, et al., (2017), Low-for-Long Interest Rates and Banks’ Interest Margins and Profitability: Cross-Country Evidence, International Finance Discussion Papers Nr. 1197, Board of Governors of the Federal Reserve System (U.S.).

See: A. Penalver, (2013), Managing Maturity Transformation Under Aggregate Uncertainty, Work in Progress, Paris School of Economics Working Paper.

Which is then often offset by interest-rate derivatives offered by those same banks. See: T. Paligorova and J.A.C. Santos, (2014), Rollover Risk and the Maturity Transformation Function of Banks, Bank of Canada Working Paper Series, Nr. 2014-8. See also recently: S. Chen, (2015), Uncertainty and Investment: The Financial Intermediary Balance Sheet Channel, IMF Working Paper, Nr. WP/15/65. Rollover risk imposes market discipline on banks’ risk-taking behavior but it can be socially costly. Chen presents a two-sided model in which a bank simultaneously lends to a firm and borrows from the short-term funding market. When the bank is capital constrained, uncertainty in asset quality and rollover risk create a negative externality that spills over to the real economy by ex ante credit contraction. Macroprudential and monetary policies can be used to reduce the social cost of market discipline and improve efficiency, she concludes. Also: C. Borio, (2016), Seven Don’ts and One Hope: The Nexus between Prudential and Monetary Policies, SUERF-Deutsche Bundesbank-IMFS Conference “SSM at 1″, Frankfurt, February 3–4. Li and Su on the other hand investigate the effects of financial globalization on bank risk by highlighting the role of rollover risk. Their conclusion is threefold: ‘(1) [i]n the short term, financial globalization will induce those banks with more short-term funding to be more risk-taking; (2) the mid-tier banks are more sensitive to the effects of financial globalization and will become more risk-taking than the rest; and (3) in the long term, financial globalization tends to affect the systemic risk in the financial system, but the precise effects depend on the country’s economic fundamentals and institutional quality.’ See: X. Li and D. Su, (2017), Effect of Financial Globalization on bank Risk: Role of Rollover Risk, Working Paper, June 28, mimeo.

A. Segura and J. Suarez, (2012), Dynamic Maturity Transformation, CEMFI Working Paper Nr. 1105 and (2013), Recursive Maturity Transformation, CEMFI Working Paper. For a quantification of the issue see: A. Segura et al., (2014), How Excessive is Bank’s Maturity Transformation, CEMFI Working Paper. See for a model design regarding optimal debt maturity: R.M. Darst and E. Refayet, (2017), A Collateral Theory of Endogenous Debt Maturity, Finance and Economics Discussion Series Nr. 57, Washington: Board of Governors of the Federal Reserve System, May.Obviously that assumes an optimal between long- and short-term debt. Issuing both long- and short-term debt balances financing costs because different debt maturities allow firms to cater to risky promises across time to investors most willing to hold risk. Collateral explains why different types of debt are issued simultaneously but also that secured loan covenants become redundant when collateral is or becomes scarce as they act as perfect substitutes for short-term debt.

L. de Haan, and J.W. van den End, (2012), Bank Liquidity, the Maturity Ladder, and Regulation, Presentation DNB.

Y. Abdih et al., (2018), Understanding Euro Area Inflation Dynamics: Why So Low for So Long?, IMF Working Paper Nr. WP/18/188, August; Y. Abdih, et al., (2016), What is Keeping U.S. Core Inflation Low: Insights from a Bottom-up Approach, IMF Working Paper Nr. 16/124; R. Auer et al., (2017), The Globalisation of Inflation: The Growing Importance of Global Value Chains, Bank for International Settlements Working Papers, Nr. 602; F. Busetti et al., (2017), Trust, but Verify. Deanchoring of Inflation Expectations under Learning and Heterogeneity, ECB Working Paper Nr. 1994, Frankfurt; M. Ciccarelli and C. Osbat, (2018), Low Inflation in the Euro Area: Causes and Consequences, ECB Occasional Paper Nr. 181; ECB, (2017), Domestic and Global Drivers of Inflation in the Euro Area, Economic Bulletin, Issue 4, pp. 72–96.

N. Tasić and N. Valev, (2009), The Maturity Structure of Bank Credit: Determinants ad Effects on Economic Growth, Working Paper.

Within the context of the EU observations are in line with the theory highlighted: these banks transform short-term customer deposits and one- to five-year hybrid and subordinated debt liabilities into loan assets with greater than five years’ maturity. The value-weighted average maturity of the assets of these banks exceeds their liabilities by 2.07 years, with a standard deviation of 1.44 years; see in detail: G. Sher and G. Loiacono, (2013), Maturity Transformation and Interest Rate Risk in Large European Bank Loan Portfolios, Working Paper.

In fact it was demonstrated that through facilitating maturity transformation, the lender of last resort gives banks an incentive to lever, diversify and lower their lending standards. Bank leverage increases shareholder value because maturity transformation effectively allows banks to borrow against lower interest rates than their shareholders.When the gains from maturity transformation are passed on to bank customers, lending standards deteriorate. This risk-taking intensifies when the term spread is steeper and is thus procyclically related to the stance of the macroeconomy; See: M. Mink, (2011), Procyclical Bank Risk-Taking and the Lender of Last Resort, DNB Working Paper Series, Nr. 301.

B. Winters, (2012), Review of the Bank of England’s Framework for Providing Liquidity to the Banking Sector, Bank of England Reporting, pp. 79–83.

R.S. Rajan and G. Bird, (2001), Banks, Maturity Mismatches and Liquidity Crises: A Simple Model, CIES Working Paper, Nr. 0132.

Just like asset bubbles can also occur under constant monetary supply.

For an exception to that: H. Scholz, S.K.H. Simon and M. Wilkens, (2007), Maturity Transformation Strategies and Interest Rate Risk of Financial Institutions: Evidence from the German Market, Working Paper.

I.J.M. Arnold and S.E. van Ewijk, (2014), The Impact of Sovereign and Credit Risk on Interest Rate Convergence in the Euro Area, DNB Working Paper Series, Nr. 425; See also: C. Roulet, (2011), Empirical Essays on Bank Liquidity Creation and Maturity Transformation Risk, Dissertation, Université de Limoge, Economics Department, in particular pp. 21–49.

Also: M. Bucher et al., (2018), Coordination Failures, Bank Run and Asset Prices, Deutsche Bundesbank Discussion Paper Nr. 39, September 26.

In which the FI has to hold in cash only a fraction of the deposits submitted at the bank. Central liquidity can close the gap except in the event of a run, during which the liquidity lines are not sufficient. However, requiring the banks to hold equal amounts of cash relative to deposits would starve the real economy of credit and would make credit yield spike and materially increase the cost of funding.

A.D. Smith, (2009), How To Make a Run-Proof Bank, Achieving Maturity Transformation Without Fractional Reserves, Griffith University Working Paper; C. Rossi, (2012), Is Maturity Transformation the Devil’s Work or just Bedeviled, Presentation, Atlanta FED, April 12.

B. Ruprecht, O. Entrop, T. Kick and M. Wilkens, (2013), Market Timing, Maturity Mismatch and Risk Management: Evidence from the Banking Industry, Working Paper.

G. de Niccolò, A. Gamba ad M. Lucchetta, (2012), Capital Regulation, Liquidity Requirements and Taxation in a Dynamic Model of Banking, Presentation, Cleveland FED, April 13.

M.K. Brunnermeier and M. Oehmke, (2013), Predatory Short Selling, Princeton University Working Paper, who demonstrate that financial institutions may be vulnerable to predatory short selling. When the stock of a financial institution is shorted aggressively, leverage constraints imposed by short-term creditors can force the institution to liquidate long-term investments at fire-sale prices.

M.K. Brunnermeier and M. Oehmke, (2010), The Maturity Rat Race, Journal of Finance, American Finance Association, Vol. 68, Issue 2, pp. 483–521. Individual creditors can have an incentive to shorten the maturity of their own loans to the institution, allowing them to adjust their financing terms or pull out before other creditors can. This, in turn, causes all other lenders to shorten their maturity as well, leading to excessively short-term financing. This rat race occurs when interim information is mostly about the probability of default rather than the recovery in default and is most pronounced during volatile periods and crises.

D. Luttrell, H. Rosenblum and J. Thies, (2012), Understanding the Risks Inherent in Shadow Banking, A Primer and Practical Lessons Learned, Staff Paper, Dallas FED.

J. C.-F. Kuong, (2013), Self-Fulfilling Fire-Sales. Fragility of Collateralized Short-Term Debt Markets, London School of Economic Working Paper. This paper shows that collateralized lending, although optimal to reduce borrower moral hazard, can lead to multiple equilibria and endogenous aggregate risk. This is because of a feedback loop between the risk-taking behavior of borrowers and the expected price of seized collateral in the secondary market. When the fire-sale price of collateral is expected to be low, lenders demand more collateral and higher debt yields, making it more attractive for borrowers to engage in risk-taking ex ante (due to limited liability). The riskier pool of projects will lead to more liquidation ex post and hence more seized collateral to be sold off, justifying the expectation of low fire-sale prices.

M. Singh and J. Aitkin, (2010), The (Sizeable) Role of Rehypothecation in the Shadow Banking System, IMF Working Paper, WP/10/172.

Re-hypothecation can in general be described as the practice by banks and brokers of using, for their own purposes, assets that have been posted as collateral by their clients. Often the client will be compensated through a lower borrowing fee or rebate of costs incurred. There are many technical variations in re-hypothecation; see in detail Sect. 11.7.

See L.M. Sweet, (2010), Central Counterparties: Understanding Risks and Risk Transformation, Presentation, NY FED, October 21.

V. Maurin, (2014), Re-Using the Collateral of Others. A General Equilibrium Model of Rehypothecation, European University Institute Working Paper; M. Katagiri, R. Kato and T. Tsuruga, (2013), Prudential Capital Controls or Bailouts: The Impact of Different Collateral Constraints Assumptions, Kobe University Working Paper.

Pecuniary externalities such as fire sales lead to overinvestment in illiquid assets or underprovision of liquidity. Internalizing the externality should be welfare enhancing. However, under imperfect (Cournot) competition this is far from guaranteed. In a standard model of liquidity shocks, when liquidity is sufficiently scarce, Cournot competition leads to even less liquidity and overinvestment, because price-taking agents do not internalize how their portfolio choices will affect prices after adverse shocks. The Cournot equilibrium overcorrects for the fire-sale externality and holds less capital than socially efficient. Implications for welfare and regulation therefore depend highly on the nature (i.e. sources) of the shocks and the competitiveness (i.e. the degree of price-taking behavior) of the industry considered. See in detail: T.M. Eisenbach and G. Phelan, (2018), Cournot Fire Sales, Federal Reserve Bank of New York Staff Reports, Nr. 837, February. Cournot competition is imperfect as it essentially is a duopoly, in which two firms with identical cost functions compete with homogeneous products in a static setting. Cournot buyers decide separately how their decision impacts prices when they are a buyer or seller, and those decisions are never weighted or averaged. They do not care about aggregate price impact but the price when they ‘buy or sell’. Cournot competition can therefore mitigate or exacerbate the externality. The value of liquid assets is not affected by whether or not an agent receives a liquidity shock. Hence, what matters for price impacts of buying and selling is initial liquidity holdings, which are primarily determined by the aggregate risk of agents receiving liquidity shocks. The theory of Eisenbach and Phelan can be identified in practice. More markets and segment of industries are dominated by one or a few large firms. Simultaneously, these segments experience underinvestment by some matrixes. Their theory explains that phenomenon as ‘firms with market power will tend to under-invest when the primary shocks they face are productivity (asset-side) shocks’ (p. 3). The same holds true for financial markets which are increasingly concentrated and as a consequence liquidity holdings are inefficiently low, making effects of a moderate or severe financial crisis more damaging than would occur otherwise.

See for an ex ante identification/quantification model: B. Jones, (2014), Identifying Speculative Bubbles: A Two-Pillar Surveillance Framework, IMF Working Paper Series, Nr. WP/14/208.

The FSB acknowledges that by pointing consistently in their shadow banking reporting to the need for better and more consistent measurement tools and the complexities it brings for international cooperation as shadow banking systems in different countries have very different characteristics.

See on the justice aspect: B. de Bruin, (2017), Information as a Condition of Justice in Financial Markets: The Regulation of Credit-rating Agencies, in Just Financial Markets?: Finance in a Just Society, (ed. Lisa Hertog), Oxford University Press, Oxford.

A. Admati and M. Hellwig, (2013), The Bankers New Clothes, Princeton University Press, Princeton, pp. 81–100 and pp. 148–167.

O. Issing, J.P. Krahnen, K. Regling and W. White, (2012), White Paper, Recommendations By the Issing-Commission, Goethe University Frankfurt, p. 10.

R. Adams and J. Driscoll, (2018), How the Largest Bank Holding Companies Grew: Organic Growth or Acquisitions?

FEDS Notes, December 21, https://​doi.​org/​10.​17016/​2380-7172.​2282; L. Fan and M.D. Flood, (2018), An Ontology of Ownership and Control Relations of Bank Holding Companies, OFR Staff Discussion Papers Nr. 1, June 27.

M. King, (1985), A Pigouvian Rule for the Optimal Provision of Public Goods, NBER Working Paper, Nr. 1681; A. Tsuneki, (2002), Shadow-Pricing Interpretation of the Pigovian Rule for the Optimal Provision of Public Goods: A Note, International Tax and Public Finance, Vol. 9, Issue 1, pp. 93–104.

See for an initial analysis of the changes in the OTC market after the regulatory interventions post-financial crisis: J. Abad et al., (2016), Shedding Light on Dark markets: First Insights from the New EU-Wide OTC Derivatives Dataset, ESBR Report Nr. 11, September. Considers both interest rate derivatives, Credit Default Swaps (few are centrally cleared yet), and foreign exchange derivatives (mostly forwards, highly decentralized, limited central clearing). Alo: R. Ali et al., (2016), Systemic Risk in Derivative markets: A Pilot Study Using CDS Data, Bank of England Financial Stability Paper Nr. 38, July; M. Bardoscia et al., (2018), Multiplex Network Analysis of the UK OTC Derivatives Market, bank of England Staff Working Paper Nr. 726, May 18. A key finding of the paper is that, in extreme theoretical scenarios where liquidity buffers are small, a handful of institutions may experience significant spillover effects due to the directionality of their portfolios. S. Schreft and S. Zhang, (2018), Network Analysis: Defending Financial Stability by Design, OFR Brief Nr. 2, August 1.

A. Levels and J. Capel, (2012), Is Collateral Becoming Scarce: Evidence from the Eurozone, DNB Occasional Studies, Vol. 10, Issue 1, pp. 1–74.

Largely based on: A.C. Pigou, (1920), The Economics of Welfare, Macmillan and Co., London (4th edition, 1932) and P.A. Samuelson, (1954), The Pure Theory of Public Expenditure, Review of Economics and Statistics, Vol. 36, pp. 387–389.

R.H. Coase, (1960), The Problem of Social Cost, Journal of Law and Economics, Vol. 3 (October), pp. 1–44; R.H. Coase, (1974), The Lighthouse in Economics, Journal of Law and Economics, Vol. 17 (October), pp. 357–376.

More recently Martin Wolf (M. Wolf, (2018), We Must Rethink the Purpose of the Corporation, Financial Times, December 11) lamented on the urgent need to rethink the purpose of the corporation and its use in the economic infrastructure in the twenty-first century. This after decades of pure focus on shareholder wealth and profits as the main purpose for corporations. Exclusive shareholder focus triggers three dire outcomes that are human (profit is not an objective but the result of a purpose; substituting money with purpose triggers massive issues) economic (limited liability for corporations was to ensure they could focus on optimal capital allocation and long-term innovation) and social in nature (Coase argued that the market could be a less efficient way of organizing production due to transaction costs thereby favoring hierarchical organizations. Markets are incomplete and often inefficient). Beyond the broader conclusion that capitalism is materially broken (J. Tepper, (2018), The Myth of Capitalism, John Wiley & Sons, Hoboken), the exclusive magic of pure shareholder focus has had its best days. See also recently Larry Fink’s Letter to Shareholders 2019 (via blackrock.​com) on Purpose and Profit on the matter. As we will see later, the relationship between corporate governance and shadow banking is full of complications. Regarding the financing of innovation and the many issues that traditional lending experiences financing innovation in business models and the implication of a shift from asset-based financing to cash-flow-based financing see: H. Toxopeus et al., (2018), Financing Business Model Innovation: Bank Lending for Firms Shifting Towards a Circular Economy, Sustainable Finance Lab Working Paper, January 30.

Ph. E. Graves, (2017), Externalities, Public Goods, and Property Rights Revisited: Regulations Based on Traditional B-C Analyses Are Too Lax, Working Paper, mimeo.

Ibid. p. 11.

Liquidity shocks can have many different sources and its magnitude is dependent on many exogenous and indigenous factors from an FI’s point of view; see in detail: P.D. Karam et al., (2014), The Transmission of Liquidity Shocks: The Role of Internal Capital Markets and Bank Funding Strategies, IMF Working Paper, Nr. WP/14/207. See for a UK-based analysis of the impact on liquidity Shocks: Hills et al., (2015), International Banking and Liquidity Risk Transmission: Lessons from the United Kingdom, Bank of England, Staff Working Paper Nr. 562. They examined the impact of changes in funding conditions on UK-resident banks’ domestic and external lending. Their results suggest that, following a rise in the liquidity shock measure, UK-resident banks that grew their balance sheets quicker relative to their peers pre-crisis, decreased their external lending more, relative to other banks, and increased their domestic lending. When they accounted for country of ownership, they found that the same pattern was true for both UK-owned and foreign-owned banks, but more pronounced for UK-owned banks’ domestic and foreign-owned banks’ external lending. Also: B. Philippe et al., (2018), Money and Capital in a Persistent Liquidity Trap, Banque de France Working Paper Nr. 703, December 17.

See for the different types of liquidity exposure and liquidity concepts: K. Nikolau, (2009), Liquidity (Risk) Concepts. Definitions and Interactions, ECB Working Paper Series, Nr. 1008.

See in detail: S. Bigio, (2013), Endogenous Liquidity and the Business Cycle, Working Paper, December 18.

BIS, (2014), Liquidity Coverage Ratio Disclosure Standards and BIS, (2014), Guidance for Supervisors on Market-Based Indicators of Liquidity.

D. Roberts et al., (2018), Bank Liquidity Provision and Basel Liquidity Regulations, FRB of NY Staff Reports Nr. 852, June. There has been reduced liquidity creation by LCR banks compared to non-LCR banks, occurring mostly through greater holdings of liquid assets and lower holdings of illiquid assets. The asset-side liquidity categories and weights are calculated using two dominant models: (1) Berger-Bouwman measure (BB) and the Liquidity Mismatch Index (LMI). See N. Berger, and C. H. S. Bouwman, (2009), Bank Liquidity Creation, Review of Financial Studies, Vol. 22, Issue 9, pp. 3779–3837 and J. Bai, et al. (2018), Measuring Liquidity Mismatch in the Banking Sector, Journal of Finance, Vol. 73, Issue 1, pp. 51–93. Also C.H.S. Bouwman, (2018), Creation and Regulation of Bank Liquidity, Texas A&M University Working Paper, October 7, mimeo. It latter covers a full literature overview on bank liquidity creation. The focus is on the economics of bank liquidity creation, both in the traditional relationship banking context and in the shadow banking context. Related prudential regulation issues – pertaining mainly to capital requirements and liquidity requirements – are also discussed.

See for a technical analysis: J.W. van den End and M. Kruidhof, (2012), Modelling the Liquidity Ratio as Macroprudential Instrument, DNB Working Paper Series, Nr. 342; D.C. Hardy and Ph. Hochreiter, (2014), A Simple Macroprudential Liquidity Buffer, IMF Working Paper, Nr. WP/14/235.

See also: W.G. Choi et al., (2017), Global Liquidity Transmission to Emerging Market Economies, and Their Policy Responses, IMF Working Paper Series, Nr. WP/17/222, October. Bernales and di Filippo studied the information dynamics contained in the interaction between unsecured and collateralized money markets thereby capturing the probabilities of migration between lending segments, and probabilities of liquidity shocks. Useful information seems extractable from those interactions. See in detail: A. Bernales and M. di Filippo, (2016), The Information Contained in Money Market Interactions: Unsecured vs. Collateralized Lending, Banque de France Working Paper Nr. 598, July, Paris; P.A. Guerron-Quintana and R. Ginnai, (2019), On Liquidity Shock and Asset Prices, Bank of japan Working Paper Nr. 19-E-4, March 13. H. Degryse et al. study the impact of higher capital requirements on banks’ decisions to grant collateralized rather than uncollateralized loans. Secured lending becomes more attractive vis-à-vis unsecured lending for the affected banks as secured loans require less regulatory capital. After a shock banks required more collateralization, but less for relationship borrowers. In Detail: H. Degryse et al., (2019), To Ask or Not to Ask: Bank Capital Requirements and Loan Collateralization, Bank of England Staff Working Paper Nr. 778, February 1.

Buch et al. show that global banks transmit liquidity shocks via their network of foreign affiliates. In their case German affiliates of global US banks. They conclude threefold: during the TAF period (Federal Reserve’s Term Auction Facility), the German affiliates with US parents with higher US funding expanded their foreign assets; the overall effect is driven by affiliates in financial centers; US-dollar-denominated lending particularly increased in response to the TAF program. Global banks spread liquidity through their systems. See: C. Buch et al., (2018), Crisis and Rescues: Liquidity Transmission through Global Banks, International Journal for Central Banking, September, pp. 187–228.

Buch and Goldberg summarized the common methodology and results of empirical studies conducted in 11 countries to explore liquidity risk transmission. Among the main results are, first, that the explanatory power of the empirical model is higher for domestic lending than for international lending. Second, how liquidity risk affects bank lending depends on whether the banks are drawing on official-sector liquidity facilities. Third, liquidity management across global banks can be important for liquidity risk transmission into lending. Fourth, there is substantial heterogeneity in the balance sheet characteristics that affect banks’ responses to liquidity risk; See: C.M. Buch and L.S. Goldberg, (2014), International Banking and Liquidity Risk Transmission: Lessons from Across Countries, Federal Reserve Bank of NY, Staff Reports, Nr. 675. Also: C. Gauthier, (2015), Emergency Liquidity Facilities, Signalling and Funding Costs, Bank of Canada Staff Working Paper Nr. 44, December.

Deterioration of collateral quality is typically associated with higher interbank rates both in the unsecured and report segments. Reducing interbank market volatility restores confidence and is welfare enhancing. See in detail: M. Wolski and M. van de Leur, (2016), Interbank Loans, Collateral and Modern Monetary Policy, ECB Working Paper Series Nr. 1959, September.

Haircut, which can have different meanings, can be defined here as the percentage by which an asset’s market value is reduced for the purpose of calculating capital requirement, margin and collateral levels; see BIS, (2013), Basel III: The Liquidity Coverage Ratio and Liquidity Risk Monitoring Tools, bis.​org, pp. 13–15.

BIS, (2008), Principles for Sound Liquidity Risk Management and Supervision, bis.​org

See for an extensive conceptual analysis: P. Smaga, (2014), The Concept of Systemic Risk, LSE/SRC Special Paper Nr. 5, August. He defines systemic risk as ‘the risk that a shock will result in such a significant materialization of (e.g. macrofinancial) imbalances that it will spread on the scale impairing the functioning of financial system and to the extent that it adversely affects the real economy (e.g. economic growth). The blueprint intends to break down and clearly categorize the processes of accumulation, materialization and spreading of systemic risk’.

Butnik and Bochmann wonder how do capital and liquidity buffers affect the evolution of bank loans in periods of financial and economic distress? They find that banks with high capital and liquidity buffers show a more muted response in their lending to adverse real economy shocks. Capital and liquidity buffers also affect bank responses to monetary policy shocks. High bank capitalization reduces the degree to which banks increase the average duration of loans to the non-financial corporate sector, while high bank liquidity strengthens the positive response to policy easing of both long- and short-term loans to the non-financial corporate sector. In detail: K. Budnik and P. Bochmann, (2019), Capital and Liquidity Buffers and the Resilience of the Banking System in the Euro Area, ECB Working Paper Nr. 2120, 22 December.

D. Diamond and P. Dybvig, (1983), Bank Runs, Deposit Insurance, and Liquidity, Journal of Political Economy, Vol. 91, pp. 401–419.

M. Brunnermeier, (2009), Deciphering the Liquidity and Credit Crunch 2007–2008, Journal of Economic Perspectives, Vol. 23, pp. 77–100. F. Allen, A. Babus, and E. Carletti, (2010), Financial Connections and Systemic Risk, NBER Working Paper, Vr. 16177.

E. Perotti, J. Suarez, (2009), Liquidity Insurance for Systemic Crises, CEPR Policy Insight 31, February. Overnight (repo) secured credit feeding the final stage of the securitization wave grew explosively during 2002–2007 to a volume over $10 trillion. Rapid withdrawals forced an unprecedented liquidity support by central banks, undermining their control over the money supply. The need to contain the future accumulation of liquidity is thus a core challenge for macroprudential policy; see G. Gorton, (2009), Slapped in the Face by the Invisible Hand: Banking and the Panic of 2007, paper prepared for the Federal Reserve Bank of Atlanta’s 2009 Financial Markets Conference, May.

E. Perotti, and J. Suarez, (2011), A Pigovian Approach to Liquidity Regulation, CEPR Discussion Paper, Nr. 8271, March. The paper has been re-issued and updated on a number of occasions; see E. Perotti, and J. Suarez, (2011), A Pigouvian Approach to Liquidity Regulation, IMF, Paper presented at the 12th Jacques Polak Annual Research Conference, November 10–11; E. Perotti and J. Suarez, (2011), A Pigovian Approach to Liquidity Regulation, DNB Working Papers nr. 291, Netherlands Central Bank, Research Department; E. Perotti and J. Suarez, (2011), A Pigovian Approach to Liquidity Regulation, Tinbergen Institute Discussion Papers 11-040/2/DSF15, Tinbergen Institute and ultimately published: E. Perotti, and J. Suarez, (2011), A Pigovian Approach to Liquidity Regulation, International Journal of Central Banking, Vol. 7, Issue 4, pp. 3–41. See for earlier work: E. Perotti and J. Suarez, (2010), Liquidity Risk Charges as a Macro-Prudential Tool, DSF Policy Paper Series, Nr. 1.

M.L. Weitzman, (1974), Prices vs. Quantities, Review of Economic Studies, Vol. 41, pp. 477–491. The method used is linked to the discussion started by Poole (1970): that methodology concerned the optimality of price or quantity of monetary policy instruments when the system is exposed to a variety of negative or idiosyncratic risks; see W. Poole, (1970), Optimal Choice of Monetary Policy Instruments in a Simple Stochastic Macro Model, Quarterly Journal of Economics, Vol. 84, pp. 197–216.

E. Perotti and J. Suarez, (2002), Last Bank Standing: What Do I Gain if You Fail, CEMFI Working Paper.

Loose monetary conditions in recent years have raised concerns regarding the allocation of credit to risky investors and the potential long-term effect on financial stability. There is actual evidence that periods of low interest rates and easy financial conditions may lead to a decline in lending standards and increased risk taking. See for an interesting analysis of corporate credit allocation: IMF, (2018), The Riskiness of Credit Allocation: A Source of Financial Vulnerability, in Global Financial Stability Report 2018: A Bumpy Road Ahead, Chapter 2, pp. 57–92. The analysis looks at the evolution of the riskiness of corporate credit allocation—that is, the extent to which riskier firms receive credit relative to less risky ones, its relationship to the strength of credit expansions, and its relevance to financial stability analysis. So, not only the volume of credit growth but the distribution of credit across lenders with a variety of profiles. They find the riskiness of credit allocation rises during periods of fast credit expansion, especially when loose lending standards or easy financial conditions occur concurrently. The riskiness of credit allocation increased in the years preceding the global financial crisis and peaked shortly before its onset. It declined sharply after the crisis and rebounded after the crisis to land well above historical averages in 2019. An increase in the riskiness of credit allocation signals heightened downside risks to GDP growth and a higher probability of banking crises and banking sector stress; it constitutes an autonomous source of financial vulnerability. Also on the latter: D. Kirty, (2018), Lending Standards and Output Growth. IMF Working Paper Nr. WP/18/23, International Monetary Fund, Washington, DC; T. Adrian and N. Liang, (2018), Monetary Policy, Financial Conditions, and Financial Stability. International Journal of Central Banking, Vol. 14, Issue 1, pp. 73–131. On lending standards and default rates: J. Gaudêncio, et al. (2019), The Impact of Lending Standards on Default Rates of Residential Real Estate Loans, ECB Occasional Paper Nr. 220, March 19. The first study to use EU-wide data, they conclude, confirms the key influence of lending standards—in particular, loan-to-value and loan-to-income ratios at origination, original loan maturity and borrower employment status—on loan default rates. The impact of other variables, such as interest rate fixation and payment type, varies depending on the country of loan origination.

See Nabilou and Pacces who prefer quantity regulation over a Pigovian model: H. Nabilou and A.M. Pacces, (2017), The Law and Economics of Shadow Banking, in I. H. Chiu & I. MacNeil (eds.), Research Handbook on Shadow Banking: Legal and Regulatory Aspects, Edward Elgar Publishing, Cheltenham, pp. 7–46.

Net stable funding ratios (also part of the Basel III package) impose an upper threshold on short-term debt that reduces overall liquidity risk, but ‘redistributes liquidity risk inefficiently across banks’. Banks with better credit opportunities will be constrained by this, ‘while the reduced systemic risk actually encourages banks with low credit ability to expand their loan book’: see: E. Perotti, and J. Suarez, (2011), A Pigovian Approach to Liquidity Regulation, CEPR Discussion Paper, Nr. 8271, March, p. 3.

See for an alternative analysis and the value of the HQLA stamp and the impact on asset prices: L.M. Fuhrer et al., (2016), The Liquidity Coverage Ratio and Security Prices, SNB Working Paper Nr. 11. August.

See also: G. Lopez-Espinosa, A. Moreno, A. Rubia, and L. Valderrama, (2012), Short-Term Wholesale Funding and Systemic Risk, A Global CoVaR Approach, IMF Working Paper, WP/12/46; G. Lopez-Espinosa, A. Moreno, A. Rubia, and L. Valderrama, (2012), Systemic Risk and Asymmetric Responses in the Financial Industry, IMF Working Paper Series, WP/12/152, later on published in: (2015), Journal of Banking and Finance, Vol. 58, pp. 471–485.

See regarding the relationship between aggregate liquidity and banking sector fragility: M. Mink, (2016), Aggregate Liquidity and Banking Sector Fragility, DNB Working Paper Nr. 534, November. As compared to non-banks, banks adopt relatively fragile balance sheet structures characterized by leverage, maturity mismatch and asset diversification. He builds his model around the understanding that liquidity (funding) risk for banks is on aggregate lower than for non-banks and explains the banks’ regulatory equity and liquidity requirements’ resistance.

E. Perotti, and J. Suarez, (2011), A Pigovian Approach to Liquidity Regulation, CEPR Discussion Paper, Nr. 8271, March, p.4.

The going-concern value of an FI as reflected in the share price.

M.C. Keeley, (1980), Deposit Insurance, Risk, and market Power in Banking, American Economic Review, Vol. 80, Issue 5, pp. 1183–1200; D. Gale, (2010), Capital Regulation and Risk-Taking, International Journal for Central Banking, pp. 187–204.

Acharya et al. argue that dividend payouts can constitute a form of risk shifting (‘dividend payouts can shift the relative value of stakeholders’ claims across firms’) in case FIs have contingent claims outstanding against each other. Dividend payments then contain negative externalities and bank capital takes the attribute of a public good. See in detail: V.V. Acharya et al., (2016), Bank Capital and Dividend Externalities, BIS Working Papers Nr. 580, September.

A. Wischnewsky and M. Neuenkirch, (2019), Shadow Banks and the Risk-Taking Channel of Monetary Policy Transmission in the Euro Area, University of Trier, Research Paper in Economics, Nr. 3, June 6, mimeo.

T. Hellmann, K. Murdock and J. Stiglitz, (2000), Liberalisation, Moral Hazard in Banking, and Prudential Regulation: Are Capital Requirements Enough?, American Economic Review, Vol. 90, pp. 147–165. Capital requirements and other regulation imposed on traditional banks also lead to higher shadow bank lending. See: S. Gebauer and F. Mazelis, (2018), The Role of Shadow Banking for Financial Regulation, Beiträge zur Jahrestagung des Vereins für Socialpolitik 2018: Digitale Wirtschaft - Session: Macrofinance I, No. E11-V2, version May 22. Many macromodels have been developed (pp. 4–7 for overview) to depict the relationship between traditional and shadow banks from a regulatory point of view. In particular the question seems to be what role shadow banking plays for financial regulation and the effectiveness of financial regulation.

E. Perotti, and J. Suarez, (2011), A Pigovian Approach to Liquidity Regulation, CEPR Discussion Paper, Nr. 8271, March.

E. Perotti, and J. Suarez, (2011), A Pigovian Approach to Liquidity Regulation, CEPR Discussion Paper, Nr. 8271, March.

G. Gianfelice, G. Marotta and C. Torricelli, (2013), A Liquidity Risk Index as a Regulatory Tool for Systemically Important Banks, An Empirical Assessment across Two Financial Crisis, Working Paper, mimeo.

The difficulties of formulating a theory of the systemic role of bank balance sheets are indeed considerable, ranging from the question of the transmission of idiosyncratic shocks in interbank markets to the questions of the costs and benefits of maturity transformation and the inefficiency of markets with cash-in-the-market pricing; see E.-L. von Thadden, (2011), Discussion of a ‘Pigovian Tax’ to Liquidity Regulation, International Journal of Central Banking, Vol. 7, pp. 43–48. The simplicity of the model is its strength and weakness at the same time: ‘it makes it difficult to evaluate how other aspects of banking and financial markets interfere with banking liquidity and why the regulation of banking liquidity may, after all, be such a complicated and controversial problem’ (p. 44).

Systemic risk and systemic importance are two different concepts that emerged from the crisis and are now widely employed to assess the potential impact of shocks that hit a specific bank or the banking system as a whole. However, these two measures are often improperly used and misunderstandings arise. Empirically, the two measures provide different information. The measurement of systemic importance deals with the assessment of the consequences on the global financial system of the failure of a bank, whereas systemic risk contributions (often measured by MES and ΔCoVaR) try to capture the joint probability of distress of financial institutions in the presence of a systemic event (p. 31). See for a full analysis: S. Masciantonio and A. Zanghini, (2017), Systemic Risk and Systemic Importance Measures During the Crisis, Bank of Italy Working Paper Nr. 1153, December. The information they provide is almost orthogonal and does not present significant overlaps, suggesting that it is inherently different. This should not be surprising since the measurement of systemic importance is almost entirely based on balance sheet data while the measurement of systemic risk contribution mainly relies on market data (p. 32).

Interesting is the question about the chronological order between panic, bank distress and credit contraction and so on. M. Baron et al. provide some insight as they highlight in their quest to develop an informative and objective measure of the occurrence and severity of banking crises that ‘large bank equity declines predict persistent credit contractions and output gaps, after controlling for non-financial equities, even outside of banking crises defined by narrative approaches. In particular, severe bank distress without panic is associated with adverse future outcomes. Large bank equity declines tend to precede other crisis indicators, suggesting that substantial bank losses are already present at the early stages of the crisis. Finally, large bank equity declines allow us to refine existing narrative chronologies of banking crises’. See: M. Baron et al., (2018), Bank Equity and Banking Crises, Working Paper, November, mimeo, later on re-issued and modified as M. Baron et al., (2019), Salient Crises, Quiet Crises, Working Paper, April 7, mimeo. Quiet crises are defined by large bank equity declines without panics. They refine the narrative chronology of banking crisis using information from bank equity returns, rather than the old methodologies based on narrative and policy models and whose findings contradicted each other. For the interest specifically regarding the chronology of banking crises, see section VII (pp. 29 ff) of their report. Also: B. Matthew and W. Xiong, (2017), Credit Expansion and Neglected Crash Risk, Quarterly Journal of Economics Vol. 132, Issue 2, pp. 713–764 and C. Romer and D. Romer, (2017), New Evidence on the Impact of Financial Crises in Advanced Countries, American Economic Review, Vol. 107, pp. 3072–3118; A. Grodecka et al., (2018), Predictors of Bank Distress: The 1907 Crisis in Sweden, Sveriges Riksbank Working Paper Series Nr. 358, October.

Informational frictions often are the root cause of (liquidity) panics which convert into outright distress. See in detail: D. Garcia-Massia and A. Villacorta, (2016), Macroprudential Policy with Liquidity Panics, ESRB Working Paper Series Nr. 24, September.

E. Perotti and J. Suarez, (2009), Approach to Liquidity Regulation, CEPR Discussion Paper, Nr. 8271, March, pp. 3–5.

Logic would tell us that the more opaque the assets are on the balance sheet of an intermediary, the higher the rollover risk in wholesale funding markets. And indeed, greater opacity means investors form more dispersed beliefs about an intermediary’s profitability. The endogenous benefit of opacity is lower fragility when profitability is expected to be high. However, the endogenous cost of opacity is a ‘partial run’, whereby some investors receive bad private signals about profitability and run, even though the intermediary is solvent. Intermediaries choose to be transparent when expected profitability is low and opaque when expected profitability is high. Intermediaries with less volatile profitability are also more likely to choose to be opaque conclude Ahnert and Nelson. See in detail: T. Ahnert and B. Nelson, (2016), Opaque Assets and Rollover Risk, Bank of Canada Working Paper Nr. 17, April.

E. Perotti, (2010), The Governance of Macro-Prudential Taxation. See also: B. Weder di Mauro, (2010), Taxing Systemic Risk, University of Mainz, Working Paper.

C.H.S. Bouwman, (2013), Liquidity: How banks create it and How It Should Be Regulated, Wharton Financial Institutions Center Working Paper.

D. Bonfim and M. Kim, (2012), Liquidity Risk in Banking: Is there Herding?, Working Paper.

A. N. Berger, C.H.S. Bouwman, T.K. Kick and K. Schaeck, (2014), Bank Risk Taking and Liquidity Creation Following Regulatory Interventions and Capital Support, Wharton Financial Institutions Center Working Paper.

A. Ball, E. Denbee, M.J. Manning and A. Wheterilt, (2011), Intraday Liquidity: Risk and Regulation, Bank of England Financial Stability Paper, Nr. 11.

A.N. Berger, C.H.S Bouwman, T. Kick and K. Schaeck, (2010), Bank Liquidity Creation and Risk Taking During Distress, Deutsche Bundesbank Discussion Paper Series, Working Paper Nr. 2: Banking and Financial Studies, Nr. 5; J. Cao and G. Illing, (2010), Regulation of Systemic Liquidity Risk, Munich University Discussion Paper Nr. 2010-2011; E. Farhi, M. Golosov and A. Tsyvinski, (2009), A Theory of Liquidity and Regulation of Financial Intermediation, Review of Economic Studies, Vol. 76, pp. 973–992.

D. Wu and H. Hong, (2013), Liquidity Risk, Market Valuation and Bank Failures, Working Paper.

F. Allen and E. Carletti, (2011), Systemic Risk and Macro-Prudential Regulation, University of Pennsylvania Working Paper. They identify six types of systemic risk, namely: (i) common exposure to asset price bubbles, particularly real estate bubbles; (ii) liquidity provision and mispricing of assets; (iii) multiple equilibria and panics; (iv) contagion; (v) sovereign default and (vi) currency mismatches in the banking system.

Most of the literature emerged only after the 2008 financial crisis with a few notable exceptions: see J.-C. Rochet, (2004), Macroeconomic Shocks and Banking Supervision, Journal of Financial Stability, Vol. 1, Issue 1, pp. 93–110; J.-C. Rochet, (2008), Liquidity Regulation and the Lender of Last Resort, Financial Stability Review, Vol. 11, pp. 45–52. He focuses on a number of market failures that can justify liquidity regulation. These include potential problems in payment systems, moral hazard problems at the individual bank level due to opaqueness of assets, and moral hazard at the aggregate level due to expectations of a generalized bailout if there are macro shocks. While the first two can be managed by ratios the latter requires more complex intervention. More recent: M. Hoerova et al., (2018), Benefits and Costs of Liquidity Regulation, ECB Working Paper Nr.n 2169, July 13. They find that liquidity tools are beneficial but cannot completely remove the need for Lender of Last Resort (LOLR) interventions by the central bank. Full compliance with current Liquidity Coverage Ratio (LCR) and Net Stable Funding Ratio (NSFR) rules would have reduced banks’ reliance on publicly provided liquidity during the global financial crisis without removing such assistance altogether. Costs of liquidity regulation are modest.

V. Bruno and H. Shin, (2014), Cross-Border Banking and Global Liquidity, BIS Working Papers, Nr. 458. See also on the effectiveness of capital flow measures in the Asean Region: V. Bruno, I. Shin and H.S. Shin, (2015), Effectiveness of Macroprudential and Capital Flow Measures in Asia and The Pacific, Working Paper in BCBS, (2015), Cross-Border Financial Linkages: Challenges for Monetary Policy and Financial Stability, BIS Paper Nr. 82, pp. 183–196; P. Lindner et al., (2018), International Monetary Policy Spillover Through the bank Funding Channel, Deutsche Bundesbank Discussion Paper Nr. 13, May 22.

Regarding the Impact on Collateral: K. Schmidt, (2019), Does Liquidity Regulation Impede the Liquidity Profile of Collateral, ECB Working Paper Nr. 2256, March 27.

See also: C. Hu et al., (2016), Leverage Illiquidity Contagion, Working Paper, version June 2016, mimeo. Interesting about their findings are the distinction between illiquidity spillovers and information asymmetry, which drive their results considering how ‘variations in stock-level leverage lead to dynamic intraday trading behavior and illiquidity transmission’. Their insights contribute to the understanding regarding funding liquidity and the overall flight for quality effect during the deleveraging process in financial market turmoil. Also: G. Ferrara et al., (2016), Systemic Illiquidity in the Interbank Network, Bank of England Staff Working Paper Nr. 586, April. Failure to roll-over short-term funding or repay obligations when they fall due generates an externality in the form of systemic illiquidity. Also on the matter: S. Langfeld et al. (2018), Systemic Illiquidity in the Interbank Network, ESRB Working Paper Nr. 86, November 15. Failure to roll-over short-term funding or repay obligations when they fall due generates an externality in the form of systemic illiquidity, they conclude. Systemic illiquidity is minimized by a macroprudential policy that skews the distribution of liquid assets toward banks that are important in the network.

There is a ‘significant and robust positive correlation between parent banks’ and foreign subsidiaries’ default risk.’ This correlation is lower for subsidiaries that have a higher share of retail deposit funding and that are more independently managed from their parents. Host country bank regulations also influence the extent to which shocks to the parents affect the subsidiaries’ default risk. In particular, the correlation between the default risk of subsidiaries and their parents is lower for subsidiaries operating in countries that impose higher capital, reserve, provisioning, and disclosure requirements, and tougher restrictions on bank activities. See in detail: D. Anginer et al., (2016), Foreign Bank Subsidiaries’ Default Risk during the Global Crisis: What Factors Help Insulate Affiliates from their Parents?, IMF Working Paper Nr. 16/109, June.

Ph. J. König, (2015), Kiquidity Requirements: A Double-Edged Sword, International Journal of Central Banking, Vol. 11, Nr. 4, pp. 129–168.

Also the relationship between liquidity and solvency aspects and systemic risk is to a large degree still to be discovered (as well as how to reflect it all properly in stress tests): see BCBS, (2015), Making Supervisory Stress Tests More Macroprudential: Considering Liquidity and Solvency Interactions and Systemic Risk, Working Paper Nr. 29, November; H. Asgharian et al., (2017), Systemic Risk and Centrality Revisited: The Role of Interactions, Lund University Working Paper, mimeo.

F. Allen, (2014), How Should Bank Liquidity be Regulated?, Wharton School, University of Pennsylvania Working Paper.

As they are not Pigovian related they will not be discussed in detail in this context. Refer A. Milne, (2013), Register, Cap and Trade: A Proposal for Containing Systemic Liquidity Risk, Loughborough University Working Paper, who proposed a Cap and Trade model for liquidity; S. Nicoletti -Altimari and C. Salleo, (2010), Contingent Liquidity, Banca D’Italia, Occasional Papers, Nr. 70, who suggest a new category of securities to satisfy bank’s liquidity needs. These would include a Roll-Over Option Facility (ROOF) that allows the issuer, for a price, to keep the funds if at maturity a readily observable variable correlated with systemic liquidity risk (e.g. the LIBOR-OIS spread) is above a trigger threshold. At rollover the yield would reflect the current price of liquidity and credit risk, making ROOFs attractive to investors. The instrument could attenuate a liquidity crisis by reducing banks’ need to roll debt over or sell off assets, and diminish the probability of runs, if markets are convinced that banks can secure sufficient liquidity when needed thanks to the widespread use of this contingent claim. Other suggestions were made by: (1) J. Stein, (2013), Liquidity Regulation and Central Banking, Remarks made at ‘Finding the Right Balance’, 2013 Credit Markets Symposium, Federal Reserve Bank of Richmond, Charlotte, NC; he develops a framework where the market failure is that banks do not take into account all the social benefits of increased liquidity reserves in terms of enhanced financial stability and lower costs to taxpayers. The central bank acting as LOLR is one way to solve this problem. However, Stein argues that it is socially costly to use LOLR capacity because it is difficult to distinguish between illiquidity and insolvency. As a result, it may be better to have liquidity regulation. In addition, it may, in cases where high-quality collateral is in short supply, be optimal to price access to the LOLR as well; (2) M. Bech and T. Keister, (2013), Liquidity Regulation and the Implementation of Monetary Policy’, BIS Working Paper Nr. 432: They consider the effect of liquidity regulation on the implementation of monetary policy. Since monetary policy is typically implemented by central banks targeting the rate in the market for central bank reserves, liquidity regulation may change the relationship between market conditions and the interest rate. That happens in their model because banks are worried about violating the liquidity regulation and are therefore more likely to seek term funding in the market. This results in a steeper yield curve at short maturities; and (3) C. Bouwman, (2014), Liquidity: How Banks Create It and How It Should Be Regulated, A.N. Berger, P. Molyneux, and J.O.S. Wilson (eds.), The Oxford Handbook of Banking (2nd ed.), which also includes an extensive literature review: In her view, the need for regulation arises because of moral hazard associated with deposit insurance and the discount window. One of the points she stresses is the importance of the interaction between capital and liquidity regulation and the need for both to be done in concert. See also J.R. Mason, (2014), Pigouvian Principles of Externalities and Cap and Trade, Remarks Delivered before 2014 A&WMA Conference, October 29, mimeo; Economics Brief, (2017), Pigouvian Taxes, What To Do When The Interest of Individuals and Society Do Not Coincide?, The Economist, August 19.

See extensively: M. Carlson et al., (2015), Why Do We Need Both Liquidity Regulations and a Lender of Last Resort? A Perspective from Federal Reserve lending during the 2007–2009 US Financial Crisis, Finance and Economics Discussion Series, Divisions of Research & Statistics and Monetary Affairs Federal Reserve Board, Washington DC, Working Paper Nr. 2015-11 (was also released as BIS Working Paper Nr. 493 in February 2015).

The idea that banks in difficulty just wait for the central bank to step in is structurally besides the truth however. In contrast to the widespread belief that distressed banks gamble for resurrection, we document that distressed banks take actions to reduce leverage and risk, such as reducing asset and loan growth, issuing equity, decreasing dividends and lowering deposit rates, document Ben-David et al. See in detail: I. Ben-David et al., (2019), Do Distressed Banks Really Gamble for Resurrection, NBER Working Paper Nr. 25794, May. The same works for the insurance industry. Kirti documents that rather than taking on more risk, US insurers hit hard by the crisis pulled back from risk taking, relative to insurers hit less hard by the crisis. Capital requirements alone do not explain this risk reduction: insurers hit hard reduced risk within assets with identical regulatory treatment. State-level US insurance regulation makes it unlikely this risk reduction was driven by moral suasion. Other financial institutions also reduce risk after large shocks: the same approach applied to banks yields similar results. My results suggest that, at least in some circumstances, franchise value can dominate, making gambling for resurrection too risky. In detail: D. Kirti, (2018), When Gambling for Resurrection is Too Risky, ESBR Working Paper Nr. 69, February 19.

Regarding the dynamics of the collateral channel in relation to central bank haircut policies. Higher haircuts lead to magnified (relative to increased % in haircut) reduced use of that type of collateral (mostly low rated), and higher funding spreads commensurate with haircut rate increases. See in detail: N. Cassola and F. Koulischer, (2016), The Collateral Channel of Open market Operations, Banque de France Working Paper Nr. 593 May, Paris.

C. Buschmann and C. Schmaltz, (2015), Sovereign Collateral as a Trojan Horse: Why Do We Need an LCR+, Working Paper, August 19, mimeo.

W. Buiter, (2007), Lessons from the 2007 Financial Crisis, CEPR Discussion Paper, Nr. DP6596: ‘Liquidity is a public good. It can be managed privately (by hoarding inherently liquid assets), but it would be socially inefficient for private banks and other financial institutions to hold liquid assets on their balance sheets in amounts sufficient to tide them over when markets become disorderly. They are meant to intermediate short maturity liabilities into long maturity assets and (normally) liquid liabilities into illiquid assets. Since central banks can create unquestioned liquidity at the drop of a hat, in any amount and at zero cost, they should be the liquidity providers of last resort both as lender of last resort and as market maker of last resort…’; also cited in Allen, (2014), How Should Bank Liquidity be Regulated?, Wharton School, University of Pennsylvania Working Paper, p. 3. See regarding the moral dimension of systemic risk: A. James, (2017), The Distinctive Significance of Systemic Risk, Ratio Juris, Vol. 30, Issue 3, pp. 239–258. He argues that the systematic imposition of risk can be wrongful or unjust in and of itself, even if harm never ensues. There may be no one in particular to blame. We can explain both ideas in terms of what he calls responsibilities of “Collective Due Care.” Collective Due Care arguably precludes purely aggregative cost-benefit decision-making and requires one kind of ‘precautionary’ attitude in public choice.

See on a (possible) optimal relationship between capital and liquidity regulation: A. Walther, (2013), Jointly Optimal Regulation of Bank Capital and Liquidity, Cambridge University Working Paper. Also: S. Di Tella, (2017), Optimal Regulation of Financial Intermediaries, NBER Working Paper Nr. 23586, July. Di Tella argues that financial intermediaries don’t internalize that high asset prices force everyone to bear more risk. The socially optimal allocation can be implemented with a tax on asset holdings, he argues.

D. Elliott, S. Salloy and A Santos, (2012), Assessing the Cost of Financial Regulation, IMF Working Paper, Nr. 12/233, p. 43.

See in detail: N. Anderson, et al., (2015), The Resilience of Financial Market Liquidity, Bank of England Financial Stability Paper, 30 October; J. Cunliffe, (2015), Market Liquidity and Market-Based Financing, Speech to the British Bankers Association International Banking Conference; W. Dudley, (2015), Regulation and Liquidity Provision, Remarks at the SIFMA Liquidity Forum, New York City. See also regarding market liquidity, stability risk and market-making after the new regulations came in place in recent years: ESRB, (2016), Market Liquidity and Market-Making, October. Market-makers’ ability to absorb temporary order imbalances by warehousing risk for short periods of time is key for markets to function properly. Market-makers themselves may finance these positions through repo markets, providing a connection between market liquidity and funding liquidity. There have been some signs of a decrease in the availability of repo market financing. The liquidity illusion remains a constant risk to financial stability.(p. 24). Also: C. Bonner et al., (2018), Drivers of Market Liquidity-Regulation, Monetary Policy or New Players, DNB Working Paper Nr. 605, September 17.

F. Allen, (2014), Ibid. p. 24 and W. Buiter, (2007), Ibid. and J.E. Stiglitz, (2014), Tapping the Brakes: Are Less Active Markets Safer and Better for the Economy?, Presented at the Federal Reserve Bank of Atlanta 2014 Financial Markets Conference, April 15.

See for further details: European Banking Federation, (2014), Bank Leverage and its Economic Implications, Working Paper.

In the US that is more balanced between FIs and market-based lending. It is very likely that Europe will evolve in that direction, given the content of the Basel III rules.

The airline industry is a notable example of having a very high fixed-to-total cost ratio (around 85–90% depending on efficiency) requiring a load factor of around 85–86% just to break even.

There is hybrid capital with profit-dependent interest rates and floating interest rates. This category of mezzanine debt is still a minor player relative to fixed senior debt. See in detail on the category: L. Nijs, (2014), Mezzanine Financing: Tools, Applications and Total Return, Wiley & Sons, Surrey. Rebalancing leverage creates asymmetric forces, that is, when reducing leverage shareholders are biased toward selling assets relative to potentially more efficient alternatives such as pure recapitalizations. See in detail: A. Admati et al., (2018), The Leverage Ratchet Effect, Journal of Finance, Vol. 73, Issue 1, pp. 145–198; R. Gropp et al., (2019), Banks Response to Higher Capital Requirements: Evidence from a Quasi-Natural Experiment, The Review of Financial Studies, Vol. 32, pp. 266–299.

A question regularly asked is whether the higher liquidity regulations for banks would trigger higher cost of debt levels. Often the answer was affirmative. See a contrario: S. Miller and R. Sowerbutts, (2018), bank Liquidity and the Cost of Debt, Bank of England Staff Working Paper Nr. 707, January 19. They endogenize banks’ funding costs as a function of their liquidity and show how they are negatively related, therefore offsetting some of the costs from higher liquidity requirements. They find evidence for this relationship using post-crisis data for US banks, implying that liquidity requirements may be less costly than previously thought.

See for a novel approach toward the measurement of the leverage ratio: J. Villar Burke, (2015), Marginal Leverage ratio as a Monitoring Tool of Systemic Risk, Working Paper, May. He proposes an innovative approach to assessing leverage based on flows using the concept of a marginal leverage ratio, ‘which reveals the leverage related to new activities, as a valuable supplement to the traditional absolute leverage ratio. The marginal leverage ratio can be used as an early warning tool to signal potential episodes of excessive leverage and to understand if, and how, banks deleverage. Besides capturing the leveraging-deleveraging cycles better than the absolute leverage ratio, the marginal leverage ratio provides an indication of risks that a stable absolute leverage ratio can conceal’.

I. Kienna and E. Jokivuolle, (2014), Does a Leverage Ratio Requirement Increase Bank Stability?, ECB Working Paper Series, Nr. 1676. The leverage ratio induces FIs with low-risk lending strategies to diversify into high-risk investments until the leverage ratio no longer constitutes a binding capital constraint on them. More recently, it was argued, based on stress testing models, that a 4–5% Leverage ratio would constrain banks’ risk-taking earlier during financial booms, providing a consistent and more effective backstop to the RWRs (Risk-Weighted Capital Requirements): I. Fender and U. Lewrick, (2015), Calibrating the Leverage Ratio, BIS Quarterly Review, December 2015, pp. 43–58.

See for an overview of the rules regarding deduction of interest deduction (‘thin capitalization rules’): J. Blouin et al., (2014), Thin Capitalization Rules and the Multinational Firm Capital Structure, EC Taxation Papers Series, Working Paper Nr. 42. Tax deductibility in a multijurisdictional world can lead to potential distortions in the patterns of asset ownership; see for an analysis and some alternatives: M.A. Desai and D. Dharmapala, (2015), Interest Deductions in a Multijurisdictional World, Coase-Sandor Institute for Law and Economics Working Paper Nr. 725, Chicago.

Which is an option the Belgian government took through its ‘notional interest deduction program’ introduced in 2006. In recent years the program has been scaled back materially to avoid abuse and dampen the impact on the state budget. See for an update on the matter: R. de Mooij et al., (2018), Growth-Enhancing Corporate Tax Reform in Belgium, Nordic Tax Journal, Vol. 1, pp. 1–17.

See for an overview: V. Fleischer, (2011), Tax Reform and the Tax Treatment of Debt and Equity, Joint Hearing, U.S. House of Representatives, Committee on Ways and Means, U.S. Senate Committee on Finance, pp. 2–4, who suggests a thin-cap like tax on debt to remove the tax incentive to increase leverage beyond the ratio that would arise in a world without taxes and avoid regulatory arbitrage.

Fleischer, (2011), Ibid. p. 11.

J.-H. Hahm, H.S. Shin and K. Shin, (2013), Non-Core bank Liabilities and Financial Vulnerability, Journal of Money, Credit and Banking, Vol. 45, Issue 1, pp. 3–36. Their position is in line with other literature to this effect, that is, credit demand is larger than deposit growth (‘sticky’) in economic boom times. In this way, a higher incidence of noncore funding will be associated with above-trend growth in credit and compressed risk premiums.

Macroprudential policies in general tend to have and are structurally associated with lower credit growth (also with lower investment and sales growth). These effects are especially significant for small and medium enterprises (MSMEs) and young firms that are more financially constrained and bank dependent. Among MSMEs and young firms, those with weaker balance sheets exhibit lower credit growth in conjunction with the adoption of macroprudential policies, suggesting that these policies can enhance financial stability. See in detail: M. Ayyagari et al., (2018), The Micro Impact of Macroprudential Policies: Firm-Level Evidence, IMF Working Paper Nr. WP/18/267, December, including an interesting literature list (pp. 26–28). Rapid domestic credit growth and house price growth positively influence the chances of a banking crisis. As well, a crisis in other countries with high trade and financial linkages raises the crisis probability. The coordinated use of macroprudential policies can help lessen the incidence of banking crises. It was largely untested whether coordinated implementation of macroprudential policies can reduce crisis probabilities. A prudent yes seems to be the answer. See: S.M. Choi et al., (2018), Friend or Foe? Cross-Border Linkages, Contagious Banking Crises, and “Coordinated” Macroprudential Policies, IMF Working Paper Nr. WP/18/09, January. Pure information sharing, which is the contemporary way of internationalizing macroprudential policies, therefore seems inadequate.

Hahm et al., (2012), Ibid. p. 2.

But less predictive in the event of a stock market crash.

Hahm et al., (2012), Ibid. p. 47. The concept of financial vulnerability has been framed by D. Aikman et al., (2015), Mapping Heat in the U.S. Financial System, Finance and Economics Discussion Series Nr. 59, Board of Governors of the Federal Reserve System. Later on the framework has been expanded and geographically diversified: S.J. Lee et al., (2017), The Anatomy of Financial Vulnerabilities and Crises, International Finance Discussion Papers Nr. 1191, Board of Governors of the Federal Reserve System, February. The typical anatomy of the evolution of vulnerabilities before and after a financial crisis is and evolves as follows: pressures in asset valuations materialize, and a build-up of imbalances in the external, financial and non-financial sectors follows. A financial crisis is typically followed by a build-up of sovereign debt imbalances as the government tries to deal with the consequences of the crisis. Also: P. Giordani and S. Kwan, (2019), Tracking Financial Fragility, FRB of San Francisco Working Paper Nr. 6, February. Available at https://​doi.​org/​10.​24148/​wp2019-06

Concern has risen about the effectiveness of the Basel III capital buffers implemented. Occhino analyzes and proposes adopting a rule-based countercyclical buffer, that is, a buffer that is automatically lowered during recessions according to a rule. See: F. Occhino, (2018), Federal Reserve Bank of Cleveland, Economic Commentary, Nr. 2018-3, full issue. Also: R. Aliaga-Díaz, et al. (2018), Monetary Policy and Anti-Cyclical Bank Capital Regulation, Economic Inquiry, Vol. 56, Issue 2 pp. 837–858; S. Eickmeier et al., (2018), Macro-Economic Effects of Bank Capital Regulation, Deutsche Bundesbank Discussion Paper Nr. 44, December 4. Also: J. Poeschl and X. Zhang, (2018), Bank Capital Regulation and Endogeneous Shadow Banking Crisis, MPRA Working Paper Nr. 92529, December 20.

Hahm et al., (2012), Ibid. p. 28.

One of the authors had already done so in 2010; see: H.S. Shin, (2010), Non-Core Liabilities Tax as a Tool for Prudential Regulation, Policy Memo.

Measures aimed at building resilience against external financial shocks, especially against its well-known vulnerability to capital flow reversals in the banking sector and the associated disruptions to domestic financial conditions. South Korea is particularly sensitive to external capital shock in their banking system and economy. Regarding the international dimensions of financial shocks see: M. Isoré, (2016), International Propagation of Financial Shocks in a Search and Matching Environment, Bank of Finland Discussion Paper Nr. 28, November 4, Helsinki. Financial shock occurs despite flexible exchange rate regimes and substitutable financial assets. Interestingly, she distinguishes, contrary to most literature, between two types of financial shocks: (1) pure liquidity contractions and (2) shocks to banks’ capitalization costs in one country do generate international financial contagion. At this stage, the economic discipline is still figuring out what vulnerabilities to financial shocks are out there. They tell us something about country-specific fragility. See in detail: J. Fisher and L. Rachel, (2016) Assessing Vulnerabilities to Financial Shocks in some Key Global Economies, Bank of England Working Paper Nr. 636, December.

V. Bruno and H.S. Shin, (2013), Assessing Macro-Prudential Policies: Case of Korea, Scandinavian Journal of Economics, Vol. 116, Issue 1, pp. 128–157.

The macroprudential measures introduced from 2010 were aimed at moderating the procyclicality of the banking sector by dampening the fluctuations in the growth of so-called noncore bank liabilities, especially cross-border banking sector liabilities (p. 32).

V. Bruno, and H. S. Shin, (2011), Capital Flows, Cross-Border Banking and Global Liquidity, Princeton Working Paper.

Ph. Hartmann, (2017), International Liquidity, LSE Financial Markets Group Paper Series, Special Paper Nr. 247, August. He identifies five dimensions of international liquidity (p. 4). In fact there are six: (1) International financial market liquidity, (2) International funding liquidity, (3) Private monetary liquidity, (4) Central bank liquidity, (5) International payments liquidity, and (6) International public liquidity support. The particular role for regulators is to preserve money-making in international assets and channeling cash to real investments.

Bruno and Shin, (2013), Ibid. p. 2.

They confirm the earlier findings on the importance of gross capital flows between countries in determining financial conditions, especially the gross flows intermediated by the banking sector; see: C. Borio, and P. Disyatat, (2011), Global Imbalances and the Financial Crisis: Link or no Link?, BIS Working Papers, Nr. 346 and P.-O. Gourinchas, and M. Obstfeld, (2012), Stories of the Twentieth Century for the Twenty-First, American Economic Journal, Macroeconomics, Vol. 4, Issue 1, pp. 226–265; M. Obstfeld, (2012), Financial Flows, Financial Crises, and Global Imbalances, Journal of International Money and Finance, Vol. 31, pp. 469–480. Bank capital flows have also been pivotal in the European financial crisis. The credit booms in countries such as Ireland and Spain were financed primarily by capital flows through the banking sector; see: P. Lane, and B. Pels, (2011), Current Account Balances in Europe, Working Paper, Trinity College Dublin. Cross-border financial flows arise when (otherwise identical) countries differ in their abilities to use assets as collateral to back financial contracts. Financially integrated countries have access to the same set of financial instruments, and yet there is no price convergence of assets with identical payoffs, due to a gap in collateral values. Home (financially advanced) runs a current account deficit. Financial flows amplify asset price volatility in both countries, and gross flows driven by collateral differences collapse following bad news about fundamentals, document Fostel et al. Their model can explain financial flows among rich, similarly developed countries, and why these flows increase volatility. See in detail: A. Fostel et al., (2019), Global Collateral and Capital Flows, NBER Working Paper Nr. 25583, February.

E. Cerutti et al., (2017), Global Liquidity and Drivers of Cross-Border Bank Flows, Economic Policy, Vol. 32, pp. 81–125.

Two comments: (1) a bank’s capitalization influences the conditions on its corporate loans, including the volume and spread (S. Claessens et al., (2018), How do Credit Ratings Affect Bank Lending under Capital Constraints, BIS Working Paper Nr. 747, September 28, via bis.​org). Rating adjustments trigger changes in loan terms that are asymmetrical: downward adjustments increase spreads by some 40 bps and reduce committed loan sizes and maturities. By contrast, upward adjustments lead to much weaker (yet opposite) effects. Importantly, we find these effects to be stronger for smaller, riskier, and banks with less capital strength, as well as for borrowers with poorer credit quality and for non-guaranteed loans; (2) Cross-border versus local lending of global banks: the distinction between the two types of lending is important for two reasons. First, local lending is much more stable, growing more slowly during expansions and shrinking less sharply during bad times. Second, local lending has grown much more important over the past two decades. S. Avdjiev et al., (2018), What Drives Local Lending by Global Banks, BIS Working Paper Nr. 746, September 26, via bis.​org. They find that host-specific factors tend to influence local lending by global banks more strongly than owner-specific factors do. Specifically, the state of the host country’s economy and the financial health of local subsidiaries are more important than the macroeconomic conditions in parent countries and the financial condition of a bank’s parent company.

S. Avdjiev et al., (2017), The Shifting Drivers of Global Liquidity, BIS Working Paper Nr. 644, June. Also: E. Cerutti et al., (2017), Global Liquidity and Cross-Border Bank Flows, Economic Policy, Vol. 32, Issue 89, pp. 81–125; D. Puy, (2016), Mutual Fund Flows and the Geography of Contagion, Journal of International Money and Finance Vol. 60, pp. 73–93.

Ibid. S. Avdjiev et al., (2017), pp. 15 ff. Also: S. Avdjiev, et al., (2017), International Prudential Policy Spillovers: A Global Perspective, International Journal of Central Banking, Vol. 13, Issue 2, pp. 5–33; E. Cerutti and S. Claessens, (2017), The Great Cross-Border Bank Deleveraging: Supply Constraints and Intra-Group Frictions, Review of Finance, Vol. 21, Issue 1, pp. 201–236; K.J. Forbes, et al., (2017), The Spillovers, Interactions, and (Un)Intended Consequences of Monetary and Regulatory Policies, Journal of Monetary Economics, Vol. 85, pp. 1–136, January; H. Kang et al., (2017), Macroprudential Policy Spillovers: A Quantitative Analysis, IMF Working Paper Nr. WP/17/170, July.

Excessive credit growth often leads to the build-up of systemic risk to financial stability. See for a review of the instruments and an early warning framework (decision-tree): L. Alessi and C. Detken, (2014) Identifying Excessive Credit Growth and Leverage, ECB working Paper Series Nr. 1723, August. See also extensively: J. Henry and C. Kolk (eds.), (2013), A Macro-Stress Testing Framework for Assessing Systemic Risk in the Banking Sector, ECB Occasional Paper Series Nr 152, October. Also: S. Albanesi et al., (2017), Credit Growth and the Financial Crisis: A New Narrative, NBER Working Paper Nr. 23740, August. A broadly accepted view contends that the 2007–09 financial crisis in the US was caused by an expansion in the supply of credit to subprime borrowers during the 2001–2006 credit boom, leading to the spike in defaults and foreclosures that sparked the crisis. Albanesi et al. show that credit growth between 2001 and 2007 was concentrated in the prime segment, and debt to high-risk borrowers was virtually constant for all debt categories during this period. The rise in mortgage defaults during the crisis was concentrated in the middle of the credit score distribution and mostly attributable to real estate investors.

See for a review of capital flows and controls around the world in recent years: IFC Bulletin, (2017), Assessing International Capital Flows after the Crisis, IFC Bulletin Nr. 42, February. For the impact of macroprudential policies on the (increasing) resilience of countries to large and volatile capital flows see the IMF case study document: IMF, (2017), Increasing Resilience to large and Volatile Capital Flows: the Role of Macroprudential Policies- Case Studies, June 21; I. Aldasoro and T. Ehlers, (2017), Highlights of Global Financial Flows, BIS Quarterly Review March, pp. 15–23. A new measure of capital flow pressures in the form of a recast Exchange Market Pressure index was developed. The measure captures pressures that materialize in actual international capital flows as well as pressures that result in exchange rate adjustments. From that index the Global Risk Response Index was developed, which reflects the country-specific sensitivity of capital flow pressures to measures of global risk aversion. See in detail: L. Goldberg and S. Krogstrup, (2018), IMF Working Paper Nr. WP/18/30, January.

Traditionally globalizing capital flows are seen as a two-way street: from advanced to emerging economies (i.e. downhill capital flows) and from emerging economies to advanced economies (uphill capital flows). The latter is more recent in nature and its magnitude is material these days. Both include reserve accumulation and private capital flows and qualify as foreign official purchases; most of that liquidity is looking for ‘safe assets’ overseas. Both these have the potential to act as a transmission channel and distort monetary stability (excessive buildup of financial risks, spillovers from policies in advanced economies, inadequate global financial safety net). See for a quantification and examination: B. Csonto and C.E. Tovar, (2017), Uphill Capital Flows and the International Monetary System, IMF Working Paper Nr. WP/17/174, June. Interesting is as well their observation that the findings contradict somewhat standard economic theory indicating that ‘capital should flow from slow-growing developed countries to fast-growing developing countries’ (p. 4).

See comprehensively: V. Bruno and H.S. Shin, (2014), Globalization of Corporate Risk Taking, Princeton Working Paper, mimeo.

S. Avdjiev et al., (2018), Gross capital Flows by Banks, Corporates and Sovereigns, BIS Working Paper Nr. 760, December 4, via bis.​org. They draw some novel conclusions: First, banks in advanced economies are responsible for the high correlation between capital inflows and outflows. Second, the private sector is the main driver of the procyclicality of capital inflows. By contrast, inflows to emerging market sovereigns move countercyclically. Third, advanced economy banks and emerging market sovereigns drive the procyclicality of capital outflows. Fourth, when global risk aversion is high, private sector flows decline, while sovereign flows show no significant response; I. Shim and K. Shin, (2018), Financial Stress in lender Countries and Capital Outflows from Emerging Market Economies, BIS Working Paper Nr. 745, September 19, via bis.​org. As proxies for financial stress in lender countries, they use bank CDS spread, sovereign CDS spread and corporate bond spread. They find that when financial stress of lender countries increases, international banks decrease their lending to EMEs, which acts as a major driver of capital outflows from EMEs. In particular, financial stress in lender countries is a more important driver than the local financial conditions and macroeconomic fundamentals of EMEs. Cross-border lending to EMEs is more susceptible to financial stress in lender countries than is local lending in foreign currency. Also: M. Hoffmann et al., (2019), International Capital Flows, External Assets and Output Volatility, Journal of International Economics, Vol. 117, pp. 242–255.

R. Bannerjee et al., (2016), Self-Oriented Monetary Policy, Global Financial Markets and Excess Volatility of International Capital Flows, BIS Working Paper Nr. 540, January. However an optimal monetary policy can substantially improve the workings of the international system, even in the absence of direct intervention in capital markets through macroprudential policies or capital controls. Also: O. Jeanne and D. Sandri, (2017), Global Financial Cycle and Liquidity Management, Working paper, October, mimeo.

Typically they are classified as being either push factors like the US monetary policy stance, US economic performance, international reserves and global risk aversion or pull factors such as real GDP growth and growth differential relative to other countries. Push versus pull is often replaced by global versus domestic. See: M. Fratzscher, (2012), Capital Flows, Push versus Pull Factors and the Global Financial Crisis, Journal of International Economics, Vol. 88, Issue 2, pp. 341–356. Pagliari an Hannan observed that push factors can be more important than pull factors in explaining volatility, illustrating that the characteristics (or determinants) of volatility can be different from those of the flows levels. For instance, risk aversion can be more important than domestic macroeconomic variables like GDP growth. See: M.S. Pagliari and S.A. Hannan, (2017), The Volatility of Capital Flows in Emerging Markets: Measures and Determinants, IMF Working Paper Nr. WP/17/41, February. They also lament (correctly) about the limited amount of research available regarding the topic of capital flow volatility, but did bring together nicely everything that is around at this stage on the topic (pp. 43 ff). They also demonstrate that portfolio and bank debt flows are more volatile than FDI flows. See for an updated review including literature: S.A. Hannan, (2018), Revisiting the Determinants of Capital Flows to Emerging Markets—A Survey of the Evolving Literature, IMF Working paper Nr. WP/18/214, September. The capital flow landscape is still determined by global- and country-specific factors but the relative importance of these factors has varied over time and depends on the type of capital flow. In that context both source and host country measures have proven relevant. Also: A.R. Ghosh et al., (2016), When Do Capital Inflow Surges End in Tears?’, American Economic Review, Vol. 106, Issue 5, pp. 581–585. Also: A.R. Ghosh et al., (2017), Managing the Tide: How Do Emerging Markets Respond to Capital Flows, IMF Working Paper Nr. WP/17/69.

A measure of uncertainty and risk aversion of the markets.

Sensitivities to global factors can vary between inflows and outflows, between gross and net flows, and by flow type.

Post-crisis the focus has been on gross capital flows (prior to the crisis that was net capital flows (current account). Davis et al. find evidence of two global factors, which we call the GFCy (global financial cycle) factor and commodity price factor. These factors together account for half the variance of gross flows in advanced countries and 40% of the variance of gross flows in emerging markets. See for details: J.S. Davis, (2019), Global Capital Flows Cycle: Impact on Gross and Net Flows, NBER Working Paper Nr. 25721, April; J.S. Davis et al., (2019), Global Drivers of Gross and Net Capital Flows, FRB of Dalles, Globalization Institute Working Paper Nr. 357, March 27; M.M. Habib and F. Venditti, (2019), The Global Capital Flows Cycle: Structural Drivers and Transmission Channel, ECB Working Paper Nr. 2280, May 15; P. McQuade and M. Schmitz, (2019), America First? A US-Centric View on Global Capita Flows, ECB Working Paper Nr. 2238, February 13.

E. Cerutti et al., (2017), How Important is the Global Financial Cycle? Evidence from Capital Flows, IMF Working Paper Nr. WP/17/193, September, including an interesting literature list (pp. 25 ff). The lack of visibility is disappointing in terms of the usefulness for emerging market policy design. In case the GFCy would explain much of the variation in capital flows it would be difficult for emerging market policy design to manage their economies as the GFCy would be driven by common shocks including factors that would have emanated from the center (a larger country like the US, e.g.) and would lead to large capital flow fluctuations. Capital controls could then insulate them from the variations at the expense of international financial integration. Would the GFCy not explain most of the variation in capital flows, which it didn’t, small and emerging countries have greater degrees of freedom to manage their economies, at least in terms of the impact of the GFCy on capital flow fluctuations.

See for a break-down of the liquidity shortage during the financial crisis: F. Dong et al., (2017), Flight to What? Dissecting Liquidity Shortages in the Financial Crisis, Federal Reserve Bank of St. Louis Working Paper Nr. 25B, October. Their model reveals that ‘a sharp reduction in the quality, instead of the liquidity, of private assets was the culprit of the recent financial crisis’. They also comment on the fact that the provision of public liquidity endogenously affects the provision of private liquidity and the policy implications.

J. Dow and J. Han, (2014), Contractual Incompleteness, Limited liability and Bubbles, Swedish House of Finance Research Paper, Nr. 14/03.

And it raises systemic risk in the market and for banks in particular: M. Brunnermeier et al., (2017), Asset Price Bubbles and Sytemic Risk, CEPR Discussion Paper Nr. DP12362. They analyze the effects of asset price bubbles on systemic risk and show that asset price bubbles in stock and real estate markets raise systemic risk at the bank level. The strength of the effect depends strongly on bank characteristics (bank size, loan growth, leverage and maturity mismatch) as well as bubble characteristics (length and size). In a median real estate bust, systemic risk increases by almost 70% of the median for banks with unfavorable characteristics. These results emphasize the importance of bank-level factors for the buildup of financial fragility during bubble episodes, document Brunnermeier et al. See in detail: M.K. Brunnermeier, (2019), Asset Price Bubbles and Systemic Risk, NBER Working Paper Nr. 25775, April.

There is a relationship between asset price bubbles and systemic risk. Systemic risk of banks rises already during a bubble’s buildup phase, and even more so during its bust. The increase differs strongly across banks and bubble episodes. It depends on bank characteristics (especially bank size) and bubble characteristics.

See for an extensive analysis of the different types of bubbles and their economic impact after deflation: Ò. Jordà et al., (2015), Leveraged Bubbles, CESifo Working Paper Nr. 5489, August.

J. Cochrane, (2014), Towards a Run-Free Financial System, Chicago Booth School of Business Working Paper, mimeo.

N. Kocherlakota, (2010), Taxing Risk and the Optimal Regulation of Financial Institutions, Economic Policy Paper Nr. 10-3, Federal Reserve Bank of Minneapolis. Perotti and Suarez Ibid. follow in that tradition. It has also created alternative movements, for example those who suggest a cap-and-trade model: See in detail: J. Stein, (2012), Monetary Policy as Financial Stability Regulation, Quarterly Journal of Economics, Vol. 127, pp. 57–95 and J. Stein, (2014), Incorporating Financial Stability Considerations into a Monetary Policy Framework, Speech, March 21.

J. Cochrane, Ibid. p. 19.

J. Cochrane, Ibid. p. 19. However there seem to be limits to this argument. Recent behavioral literature suggests that explicit financial penalties/rewards may undermine willingness to behave pro-socially. See: B. Lanz et al., (2017), The Behavioral Effect of Pigouvian Regulation: Evidence from a Field Experiment, Research Brief MIT Center for Energy and Environmental Policy Research, online via ceepr.​mit.​edu (also as extended research paper in the Research Paper Series nr. 32 of the Graduate Institute Geneva). They conclude that ‘experimental results show that a monetary incentive explicitly motivated by the regulation of carbon emissions is less effective as compared to a neutrally framed price change of the same magnitude. This is evidence of negative behavioral effects associated with price-based regulation of externalities. We further observe that the extent of behavioral effects varies with substitutability: for products with close substitutes (cola and milk in our setting), we observe very substantial negative behavioral effects, while for products where substitution requires more effort (spreads and meat) behavioral effects are small and statistically indistinguishable from zero.’ The implication is that a Pigovian tax would need to be set above its socially efficient level (i.e. the cost of the damages produced) in order to compensate for the discussed behavioral effect. See also: D. Nerudová and M. Dobranschi, (2016), Procedia- Social and Behavioral Sciences, Vol. 220, May 31, pp. 302–311. See somewhat in line: P. Fitzgerald et al., (2016), Will I Pay for Your Pleasure? Perceptions of Negative Externalities and Responses to Pigovian Taxes, Working Paper, mimeo.

Quantitative capital ratio regulations quickly lead to arguments and games. Should the denominator be ‘risk-weighted’ assets or total assets, or should the capital requirement be based on a full-Value-at-Risk model? The total-assets approach has a satisfying simplicity. See in detail: D. Duffie, (2014), Is Keeping It Simple for Banks Stupid?, Bloomberg View, January 7 and D. Duffie, (2010), How Big Banks Fail and What to Do about It, Princeton University Press, Princeton, NJ.

See also: N. Tideman and F. Plassmann, (2016), Efficient Bilateral Taxation of Externalities, Working Paper, version August 15, mimeo. See regarding the administrative cost of applying Pigovian taxes, in particular in the case of multiple externalities: D. Jaqua and D. Schaffa, (2016), Pigouvian Taxation with Costly Administration and Multiple Externalities, Working Paper, version: August 27, mimeo. Their two main conclusions are ‘[i]f administrative cost varies only with the pollution generating activity, the optimal tax is equal to the externality added to the marginal administrative cost, and the private market fully internalizes the externality and administrative cost. If, due to the nature of enforcement, administrative cost varies with tax rates, then optimal policy leaves some portion of externalities uncorrected. As a result, using taxes to modify complement and substitute activities will be welfare increasing.’

Cochrane, Ibid. p. 20.

I. Ben-David, F. Franzoni and R. Mousawai, (2014), Do ETFs increase Volatility, NBER Working Paper, Nr. 20071 (later on published in updated form as I. Ben-David, F. Franzoni and R. Mousawai, (2018), Do ETFs increase Volatility, The Journal of Finance, Wiley library online). They study whether exchange traded funds—an asset of increasing importance—impact the volatility of their underlying stocks. Using identification strategies based on the mechanical variation in ETF ownership, they present evidence that stocks owned by ETFs exhibit significantly higher intraday and daily volatility. They estimate that an increase of one standard deviation in ETF ownership is associated with an increase of 16% in daily stock volatility. The driving channel appears to be arbitrage activity between ETFs and the underlying stocks. Consistent with this view, the effects are stronger for stocks with lower bid-ask spread and lending fees. The evidence further suggests that ETF ownership increases stock turnover, which in turn suggests that ETF arbitrage adds a new layer of trading to the underlying securities. Also: K. Pan and Y. Zeng, (2017), ETF Arbitrage under Liquidity Mismatch, ESRB Working paper Series Nr. 59, December. They conclude that ‘[a] natural liquidity mismatch emerges when liquid exchange traded funds (ETFs) hold relatively illiquid assets’ and that ‘that this liquidity mismatch can reduce market efficiency and increase the fragility of these ETFs’. Regarding the relation between volatility and systemic risk: S. Malik and Peter Lindner, (2017), On Swing Pricing and Systemic Risk Mitigation, IMF Working Paper Nr. WP/17/159. It has become clear that the FSB definition (both large and narrow) will only take us so far in terms of defining what is included or not. The EC has structurally included investments funds including ETFs. See also: P.F. Hanrahan, (2018), Exchange Traded Funds in the Shadow Banking System, in Research Handbook on Shadow Banking, Legal and Regulatory Aspects (eds. I. H.-Y. Chiu and I.G. MacNeil), Edward Elgar Publishing, Cheltenham, UK, pp. 363–396.

And related categories ETNs (Exchange Traded Notes) and the wider category of Exchange traded Products (ETPs). In the background plays the marketplace where the passive investment thesis has now become dominant. Large swings in investor sentiment can have a profound effect on the stability of the marketplace when being dominated by passive ETF and other products. The media impact in equity prices and volatility has now been properly documented. See: S.P. Fraiberger et al., (2018), Media Sentiment and International Asset Prices, IMF Working Paper Nr. WP/18/274, December. In particular changes in global news sentiment have a large(r) impact on equity returns around the world, which does not reverse in the short run. Positive news creates short-term equity inflows, negative news creates long-term (i.e. permanent) equity outflows. Also: A. Ghosh and G.M. Constantinides, (2017), What Information Drives Asset Prices, NBER Working Paper Nr. 23689, August. Regarding the systemic risk nature of ETFs see: M. Pagano et al., (2019), Can ETFs Contribute to Systemic Risk? Reports of the Advisory Scientific Committee, Nr. 9, June 17. The short answer: yes, and it is further linked to increased price volatility, used as an arbitrage mechanism and associated with greater co-movement of asset prices.

Long-term investors on the other hand work countercyclical and thereby improve financial stability and reduce volatility. See: T. Fong et al. (2018), Do Long-Term Institutional Investors Contribute to Financial Stability? – Evidence from Equity Investment in Hong Kong and International Markets, HK Institute for Monetary Research Working Paper Nr. 22, September.

F. Zhang, (2010), High Frequency Trading, Stock Volatility and Price Discovery, Yale School of Management Working Paper. It contrasts however with a more recently but commercially commissioned study by the Futures Industry Association: N.P.B. Bollen and R.E. Whaley, (2013), Futures Market Volatility, Vanderbilt University, Owen Graduate School of Management Working Paper. The latter found that there is ‘no evidence to suggest that realized return volatility in electronically-traded futures markets has changed through time.’… ‘[w]e now have empirical evidence that volatility in the futures markets has neither increased nor decreased once the effects of macro-economic shocks are removed.’ Also: FCA, (2016), Are High-Frequency Traders Anticipating the Order Flow? Cross-Venue Evidence from the UK Market, Occasional Paper Nr. 16, September. Illiquidity brings more illiquidity, contrary to the dynamic that in normal market conditions illiquidity, by moderating the demand for immediacy, produces a self-stabilizing effect, highlight Cespa and Vives. In a crash, they indicate ‘an increase in illiquidity fosters a disorderly “run for the exit,” accentuating the price movement’… ‘this source of fragility (can be traced back, ed.) to the fact that modern markets’ pervasive presence of automated trading hampers market participants’ access to (i) trade, and (ii) quote information. That is, two frictions shape trading activity nowadays: a participation friction (not all liquidity suppliers are continuously present in the market), and an informational friction (not all traders have access to timely market information).’ See in detail: G. Cespa and X. Vives, (2017), High Frequency Trading and Fragility, ECB Working Paper Nr. 2020, February.

See B. Biais, T. Foucault and S. Moinas, (2014), Equilibrium Fast Trading, Working Paper. They argue that high-speed market connections and information processing improve the ability to seize trading opportunities, raising gains from trade. They also enable fast traders to process information before slow traders, generating adverse selection, and thus negative externalities. When investing in fast-trading technologies, institutions do not internalize these externalities. Besides banning them altogether, a Pigovian tax is suggested on the technology that enables high-speed trading.

Margin-based investing is essentially investing with someone else’s capital (often that of your bank or broker, who provides the capital as a debt facility) while depositing only a limited amount of collateral yourself, in case your trade goes wrong but you still have to repay your debt to your broker. It has been proven that pure command-and-control regulation seems to have little effect on volatility. A countercyclical Pigovian instrument seems to dampen volatility better caused by the collateral constraint through margin lending. See in detail: J. Brumm et al., (2014), Margin Regulation and Volatility, ECB Working Paper Series, Nr. 1698; M. Bakoush et al., (2017), Margin Requirements and Systemic Liquidity Risk, University of Southampton Working Paper, November, mimeo. They find that ‘distress due to margin procyclicality in the derivatives market can spillover to the interbank market leading to systemic liquidity risk. Interconnectedness further amplifies the effects of systemic risk within the interbank market. The model shows that central clearing might increase the possibility of systemic liquidity risk due to tight margin requirements and the timing of cash flows required from banks.’ See in detail on the aspect of central clearing chapter 5 (Volume 1). Also: O. Brossard, and S. Saroyan, (2016), Hoarding and Short-Squeezing in Times of Crisis: Evidence From the Euro Overnight Money Market, Journal of International Financial Markets, Institutions and Money, Nr. 40 (Supplement C), pp. 163–185. A. Eross, et al., (2016), Liquidity Risk Contagion in the Interbank Market, Journal of International Financial Markets, Institutions and Money, Nr. 45 (Supplement C), pp. 142–155; D. Murphy, et al., (2016), A Comparative Analysis of Tools to Limit the Procyclicality of Initial Margin Requirements, Bank of England Staff Working Paper Nr. 597, London; P. Tasca, and S. Battiston, (2016), Market Procyclicality and Systemic Risk, Quantitative Finance, Vol. 16, Issue 8, pp. 1219–1235; M. Bakoush et al., (2017), Margin Procyclicality and Systemic Risk, University of Southampton Working Paper, mimeo.

H. Zhu, (2014), Do Dark Pools Harm Price Discovery, Review of Financial Studies, Vol. 27, Issue 3, pp. 747–789. Dark pools are equity trading systems that do not publicly display orders. They offer potential price improvements but do not guarantee execution. Zhu concludes that the existence of dark pools tends to improve the price discovery function on the official exchanges (though it also leads to reduced exchange liquidity), but draws uninformed investors into the dark pools. He further concludes that the effect of the dark pool on price discovery can become weaker the longer the information horizon, and that although the dark pool can improve price discovery on average, it can harm price discovery in some rare realizations of uninformed order imbalance. See in general S. Patterson, (2013), Dark Pools: The Rise of the Machine Trader and the Rigging of the U.S. Stock Market, Crown Business, New York and M. Lewis, (2014), Flash Boys: A Wall Street Revolt, W.W. Norton & Company, New York; FCA, (2016), Asymmetries in Dark Pool Reference Prices, Occasional Paper 21, September; L. Ye, (2016), Understanding the Impact of Dark Pools on Price Discovery, Working Paper, December 27; M. Petrescu and M. Wedow, (2017), Dark Pools in European Equity Markets: Emergence, Competition and Implications, Occasional Paper Series, Working Paper nr. 193, July; B. P. Baxter, (2017), The Securities Black Market: Dark Pool Trading and the Need for a More Expansive Regulation ATS-N, Vanderbilt Law Review, Vol. 70, pp. 311–337.

See a contrario: A. Beber et al., (2018), Short-Selling Bans and Bank Stability, ESRB Working Paper Series Nr. 64, January. They conclude that financial institutions whose stocks were banned experienced greater increases in the probability of default and volatility than unbanned ones, and these increases were larger for more vulnerable financial institutions.

Z. Pozsar and M. Singh, (2011), The Nonbank-Bank Nexus and the Shadow Banking System, IMF Working Paper, WP/11/289.

The objective here is not to provide a full analysis of shadow banking but only to focus on the externality-inducing activities embedded in the shadow banking sector.

See supra regarding the different methodologies and schemes to assess the size of the shadow banking sector.

See regarding the (stabilizing) role of central banks in matching bank liquidity: L. Vincent et al., (2018), Stabilising Virtues of Central Banks: (Re) Matching Bank Liquidity, Banque de France Working Paper Nr. 666, March 7.

R. Ayadi et al., (2016), Regulatory Arbitrage in EU Banking: Do Business Models Matter, IRCCF Working Paper, July. Different business models lead to different levels of bank risk (measured by distance to default). The choice between business models is driven by regulatory arbitrage and once the choice is made, the choice of business model drives the intensity of future regulatory arbitrage engaged into. Also: R. Ayadi, et al., (2016), Bank Business Models Monitor, 2015, Europe, IRCCF, HEC Montreal; R. Ayadi, (2016), Bank Business Models in Europe: Why Does it Matter for the Future of Regulation and Resolution, IRCCF Working paper, July. See regarding the impact of extreme monetary policies on business models A. Lucas et al., (2017), Bank Business Models at Zero Interest Rates, ECB Working Paper Nr. 2084 June. Another reminder of the legal dimensions of regulatory arbitrage and the reasons behind the failure of global financial regulation: A. Rise, (2014), Managing Regulatory Arbitrage: A Conflict of laws Approach, Cornell International Law Journal, Vol. 47, pp. 63–119. Her position is to treat conflicts in law as arbitrage in finance. Rather than focusing on the gap created by differences in national legislation, they are seen as conflicts and can be resolved as such, closing the opportunity gap.

Also here the question arises to what degree counterparty arrangement poses a threat to financial stability. First it sould be observed that interconnectedness is not very well understood in the (re)insurance sector. Models of the complex interactions among reinsurers and with other participants in the financial system and the real economy are at a very early stage of development. The market remains opaque including the counterparty arrangements among (re)insurers known as retrocession agreements. Retrocession agreements involve risk sharing among reinsurers and are an important source of off-balance-sheet capital. These agreements create a complicated network of institutions exchanging their risk exposures in order for each institution to achieve a desired risk profile. An important feature of the retrocession market is its opacity. Davison et al. have been looking into this and conclude that ‘[r]einsurers generally have detailed information on their direct counterparty but not necessarily on their counterparty’s retrocession arrangements. This exchange of risk has the potential to result in a transfer of exposures to the same set of players. A reinsurance spiral can occur when reinsurers extensively underwrite each other’s risk exposures, possibly resulting in the circulation of exposures among the same set of players.’ They make an attempt to model the arrangements consistent with the financial statement data of the reinsurers involved and is then stress-tested. They then apply a banking-based network model approach to the insurance industry. They then assess the interconnectedness among reinsurers based on potential claims (rather than premiums). They conclude that contagion is very likely but that reinsurance spirals are rare, and that further research is needed. They were able to conclude however that ‘the size of the potential market disruption is sensitive to (1) the distribution of risk among counterparties, (2) the trigger for financial distress, (3) the time horizon for claims resolution and (4) the degree of loss netting’. See in detail: M. Davison et al., (2016), Are Counterparty Arrangements in Reinsurance a Threat to Financial Stability, Bank of Canada Working Paper Nr. 39, August.

See in detail: B.M. Lawsky, (2013), Shining a Light on Shadow Insurance: A Little-known Loophole That Puts Insurance Policyholders and Taxpayers at Greater Risk, New York State Department of Financial Services Report; R.S.J. Kooijen and M. Yogo, (2013), Shadow Insurance, NBER Working Paper, Nr. 19568. See further: E. Berdin, and M. Sottocornola, (2015), Insurance Activities and Systemic Risk, Goethe-University, Frankfurt, ICIR Working Paper Nr. 19/15; C. Bierth, et al., (2015), Systemic risk of insurers around the globe. Journal of Banking & Finance, Vol. 55, pp. 232–245; C. Kaserer, and C. Klein, (2017), Systemic Risk in Financial Markets: How Systemically Important are Insurers? TU Munich, Working Paper, mimeo; D. Kessler, (2013), Why (Re)Insurance is Not Systemic, Journal of Risk and Insurance, Vol. 81, Issue 3, pp. 477–487; R.S.J. Koijen, and M. Yogo, (2016), Shadow Insurance, Econometrica, Vol. 84, Issue 3, pp. 1265–1287; and F. Irresberger and Y. Peng, (2016), Why Do Life Insurers use Shadow Insurance, Working Paper, November 19, mimeo. Shadow insurance (i.e., reinsure their risks using affiliated, unauthorized and unrated off-balance sheet entities rather than traditional reinsurers) leads to greater market shares, reduced leverage, and improved risk-based capital ratios. Hepfer et al. document that shadow insurance arrangements are concentrated in tax haven subsidiaries and are associated with significant tax savings. Since shadow insurance is also associated with larger cash payouts to shareholders, shadow insurance can be seen as ‘tax motivated’. The model operates as such that reserves that are held to pay out future policy claims can now be freed up for other purposes (shareholder payouts, investment and compensation). See: B.F. Hepfer et al., (2016), Taking Shadow Insurance Out of the Shadows: Regulatory Arbitrage, Taxes and Capital, Working Paper, September, mimeo; D. Zafeiris, (2018), The Role of Regulation and Supervision in Shadow Banking: An Insurance Sector Perspective, in Shadow Banking: Financial Intermediation beyond Banks (Ed. Esa Jokivuolle), SUERF Conference Proceedings 2018/1, Larcier, pp. 60–65.

IMF, (2016), Global Financial Stability Report. Potent Policies for a Successful Normalization, Chapter 3: The Insurance Sector-Trends and Systemic Risk Implications, pp. 87–118. Some of the conclusions include that ‘[l]ife insurers’ contribution to the aggregate capital shortfall and Value at Risk of the financial sector has returned to historically high levels and may remain elevated if interest rates continue to be low for long’ and that ‘together with banks, insurers are important transmitters of volatility spillovers across financial sectors and across regions, and they have become more central to the financial systems of North America and advanced Asia’. The exposure to aggregate risk as a result of maturity mismatches has risen for the sector (pp. 107–108).

See in detail: FSB, (2013), Strengthening Oversight and Regulation of Shadow Banking Policy Framework for Strengthening Oversight and Regulation of Shadow Banking Entities, pp. 6–11.

A tremendous problem still is the data availability of the sector and the consistency in reporting terms on a per-country basis. That is particularly relevant as shadow banking markets differ significantly in nature and content between countries. One can study them one by one, country by country or one can look for patterns and comparative analysis. See for the latter: S. Wei, (2018), Demystifying Shadow Banking in a Bank-Centric Paradigm: A Comparative Approach, Banking & Finance Law Review, Vol. 33, Issue 1, pp. 1–39. The comparative study helps pierce the veil of shadow banking by looking into the foundational contrasts as well as the dynamic interconnections between the shadow and regular banking sectors, which further deepen the understanding of the potential regulatory logic of shadow banking. Including a specific sub-segment on China shadow banking markets.

A. Almazan et al., (2015), Securitization and Banks’ Capital Structure, Working Paper, March, Mimeo.

S. Claessens and L. Ratnovski, (2014), What is Shadow Banking?, IMF Working Paper, WP/14/25.

M. Cipriani et al., (2015), Informational Contagion in the Laboratory, Federal Reserve Bank of NY Staff Papers Nr. 715 (March).

Claessens et al., (2014), Ibid. p. 5.

See, for instance, the model introduced recently by Harris et al.: They propose new systematic tail risk measures constructed using two different approaches. The first extends the canonical downside beta and co-moment measures, while the second is based on the sensitivity of stock returns to innovations in market crash risk. The first follow naturally from existing downside beta and co-moment measures, respectively, while the second is based on the demand of investors to hedge against extreme downside risk (p. 15). They show that tail risk is more relevant for longer horizons. See R.D.F. Harris et al., (2016), Systemic Tail Risk, Bank of England Working Paper Nr. 637, December.

Buchak et al. studied which types of activities migrate to the shadow banking sector and why migration occurs in some sectors, and not others. Focus is on the US residential mortgage market, where shadow banking accounts for half the lending volume. Banks and shadow banks compete for borrowers in this segment. Banks face regulatory constraints, but benefit from the ability to engage in balance sheet lending. Like shadow banks, banks can choose to access the securitization market. They conclude that disruptions in securitization markets rather than capital requirements have the largest quantitative impact on aggregate lending volume and pricing. In detail: G. Buchak et al., (2018), The Limits of Shadow Banks, NBER Working Paper Nr. 25149.

Claessens et al., (2014), Ibid. p. 5.

Claessens et al. provide some examples: ‘[e]xamples include, besides the general implicit guarantee provided to the “too-big-to-fail,” large banks active in shadow banking, the Federal Reserve Term Securities Lending Facility (TSLF) that backstops the collateral intermediation processes, the implicit too-big-to-fail guarantees for tri-party repo clearing banks and other dealer banks, the bankruptcy stay exemptions for repos which in effect guarantee the exposure of lenders, or the implicit, reputational and other guarantees on bank-affiliated products or on liabilities of non-bank finance companies’ (pp. 5–6).

See f.e. M.A. Rivera-Castro et al., (2016), Tail Systemic Risk and Banking Network Contagion: Evidence from the Brazilian Banking System, ICMA Centre Discussion Paper Nr. ICM-2016-05, September. Larger banks in the country like Bradesco and Itaú are the origin of the larger systemic shocks from the banking system to the financial system network. See also: T.C. Silva, et al., (2016), Network Structure Analysis of the Brazilian Interbank Market. Emerging Markets Review, Vol. 26, pp. 130–152. For a US analysis: F. Duarte and C. Jones, (2017), Empirical Network Contagion for U.S. Financial Institutions, Federal Reserve Bank of New York Staff Reports, Nr. 826, November. Network spillovers arise through default cascades. Duarte and Jones highlight that ‘[d]efault spillovers can be large when nodes inside the network are more exposed to losses outside the network or when the topology of the network implies a higher degree of connectivity among nodes’(p. 38). K. James et al., (2019), Network Contagion and Interbank Amplification during the Great Depresion, Journal of political Economy, Vol. 127, Nr.2, pp. 465–507.

It is impossible to provide a full review of the techniques, and therefore the focus will be on the externality-inducing elements of the shadow banking activities as that is what links to a Pigovian application in the shadow banking sector.

See also: A. Polanski and E. Stoja, (2016), Extreme Risk Interdependence, ESRB Working Paper Series Nr. 12, June. They decompose tail risk interdependence into systemic and residual interdependence and measure the contribution of a constituent to the interdependence of a system. Also interesting is their analysis of the symmetry of the interdependence structure in the tails (pp. 8 ff). Rigibon complements with his analysis regarding the relationship between contagion, spillover and interdependence. See in detail: R. Rigobon, (2016), Contagion, Spillover and Interdependence, Bank of England Staff Working Paper nr. 607, August. It contains a full review of the empirical literature on international spillovers and contagion. Also: R.D.F. Harri et al., (2016), Systematic Tail Risk, Bank of England Staff Working Paper Nr. 637, December.

A. Polanski and E. Stoja, (2015), Extreme Risk Interdependence, Bank of England Staff Working Paper, Nr,. 563, November. They further assess: a) tail independence; b) whether an empirical interdependence structure is generated by a theoretical model; and c) symmetry of the interdependence structure in the tails.

E. Perotti, (2013), The Roots of Shadow Banking, CEPR Paper, Nr. 69.

A. Jobst, (2008), What is Securitization, Finance and Development, September, pp. 48–49.

The drivers behind the rise in investor demand for securitized products in the run-up to the crisis have long been ignored. More recently it was identified that both agency problems and neglected risks played an important role in driving investor demand for non-traditional securitizations prior to the crisis; see S. Chernenko, S. Hanson and A. Sunderam, (2014), The Rise and Fall of Securitization, NBER Working Paper Nr. 20777.

See in detail: S. Claessens et al. (2012), Shadow Banking: Economics and Policy, IMF Staff Discussion Note, SDN 12/12, December, pp. 6–10.

See for a detailed analysis of the securitization process: Z. Poszar, (2008), The Rise and Fall of the Shadow Banking System, Regional Financial Review, July, pp. 13–25. For a detailed analysis of the intermediation process see: Z. Poszar, T. Adrian, A. Ashcraft and H. Boesky, (2013), Shadow Banking, Federal Reserve Bank of New York Economic Policy Review, December, pp. 1–16.

M. Knaup, and W. Wagner, (2010), Measuring the Tail Risks of Banks, Working Paper; W.S. Frame, L. Wall and L.J. White, (2012), The Devil is in the Tail: Residential Mortgage Finance and the U.S. Treasury, Working Paper Federal Reserve Bank of Atlanta 2012, Financial Markets Conference; M.R.C. van Oordt and C. Zhou, (2013), Systematic Tail Risk, DNB Working Paper, Nr. 400.

F. Battaglia and A. Gallo, (2012), The Impact of Securitization on Tail and Systemic Risk: Evidence From the Financial Crisis, Working Paper.

V.V. Acharya, T.F. Cooley, M.P. Richardson and I. Walter, (2010), Regulating Wall Street: The Dodd Frank Act and the New Architecture of Global Finance, Wiley and Sons, Hoboken, Chapter 4.

W. Jiangli and M. Pritsker, (2008), The Impact of Securitization on bank Holding Companies, FDIC/FRB Working Paper; W. Jiangli, M. Pritsker and P. Raupach, (2007), Banking and Securitization, FDIC Working Paper.

That happened because the demand for those products was higher than the supply despite the fact that in the years before the crisis the industry produced over 1 Trillion USD in securitized assets.

J. Brunsden et al. (2014), Draghi’s ABS-Market Revival Set for Boost from Regulators, Bloomberg.​com, September 16. Remarkably enough, there seems to be no relationship whatsoever between the size of the program suggested by the ECB and the actual securitization market in Europe (see: C. Altomonte and P. Bussoli, (2014), Asset-Backed Securities: They Key to Unlocking Europe’s Credit markets, Bruegel Policy Contribution, Nr. 2014/7) hinting at a shadow agenda on behalf of the ECB.

T. Alloway, (2014), Sliced and Diced Debt Deals Make Roaring Comeback, Financial Times, June 4.

Low or no inflation is, however, caused by the deleveraging that is ongoing at the level of households, corporations and governments alike and reflects the decrease in purchasing power in large parts of society due to unemployment and a variety of austerity measures. Low inflation is therefore totally acceptable and perfectly explainable and cannot warrant monetary intervention.

See: S. Fleming and C. Giles, (2014), Bank of England: Crashing the Party, Financial Times, June 24.

R.G. Rajan, (2005), Has Financial Development Made the World Riskier?, NBER Working paper, Nr. 11728.

Also: G. Anderson et al., (2018), Lending Relationship and the Collateral Channel, Bank of England Staff Working Paper Nr. 768, November 16. Collateral and private information are substitutes in mitigating credit frictions.

T. Piskorski, A. Seru and V. Vig, (2010), Securitization and Distressed Loan Renegotiation: Evidence from the Subprime Mortgage Crisis, Chicago Booth School of Business Research Paper, Nr. 09-02; J.P. Hunt, (2010), What Do Sub-Prime Securitization Contracts Actually Say About Loan Modification? Preliminary Results and Implications, Berkeley Center for law, Business and the Economy Working Paper. The current monetary policies also add to inequality as they favor investors in certain securities over others. See, for instance, regarding the inclusiveness of Abenomics in Japan: C. Aoyagi et al., (2015), How Inclusive is Abenomics?, IMF Working Paper Series Nr. WP/15/54, Washington D.C. They observe a ‘positive effect on average income growth, but an adverse effect on income equality’. The package of structural reforms planned under Abenomics is found to be effective in increasing both average income growth and income equality. The main policy implication of their analysis is that ‘full implementation of structural reforms– especially labor market reforms–is necessary to both foster growth and increase equality’. Also: D. Furceri et al., (2016), The Effects of Monetary Policy Shocks on Equality, IMF Working Paper Series Nr. WP/16/245, December. They focus on the effect of monetary policy shocks on income inequality. They conclude that ‘contractionary (expansionary) monetary actions increase (reduce) income inequality. The effect, however, varies over time, depending on the type of the shocks (tightening versus expansionary monetary policy) and the state of the business cycle, and across countries depending on the share of labor income and redistribution policies.’ They also evidence that ‘the effect is larger for positive monetary policy shocks, especially during expansions’… and that ‘the effect is larger in countries with higher labor share of income and smaller redistribution policies’. Please observe their literature references (pp. 22–24), which include some interesting items. More straightforward are the conclusions of D. Domanski et al., (2016), Wealth Inequality and Monetary Policy, BIS quarterly Review, March, pp. 45–64. Their simulation suggests that wealth inequality has risen since the Great Financial Crisis. While low interest rates and rising bond prices have had a negligible impact on wealth inequality, rising equity prices have been a key driver of inequality. A recovery in house prices has only partly offset this effect. Their conclusion that monetary policy ‘may’ have added to inequality can be seen I guess as professional courtesy. See a contrario: A. Samarina and A. Nguyen, (2019), Does Monetary Policy Affect Income Inequality in the Euro Area, DNB Working Paper Nr. 626, March 8. The latter find that expansionary monetary policy in the euro area reduces income inequality, especially in the periphery countries. J. Frost and R. van Stralen, (2018), Macroprudential Policy and Income Inequality, DNB Working Paper Nr. 598, June 1; A. Colciago et al., (2018), Central bank Policies and Income and Wealth Inequality: A Survey, DNB Working Paper Nr. 594, May 22.

D. Solomon, (2012), The Rise of a Giant: Securitization and the Global Financial Crisis, American Business Law Journal, Vol. 49, Issue 4, pp. 859 ff.

A.J. Levitin, (2013), The Paper Chase: Securitization, Foreclosure and the Uncertainty of the Mortgage Title, Duke Law Journal, Vol. 63, pp. 637–734. Further: securitization leads to excessive (insufficient) foreclosures in a bad state if the mortgage pool is of low (high) quality; see: J. Kuong and J. Zeng, (2015), Servicing Securitization through Inefficient Foreclosure, Working Paper, September, mimeo and S. Krueger, (2014), The Effect of Mortgage Securitization on Foreclosure and Modification, Working Paper, mimeo.

M. Pagano and P. Volpin, (2012), Securitization, Transparency and Liquidity, London Business School Working Paper.

X. Dou and J. Wang, (2014), Asset Securitization and Bubbles: An Illustration of Subprime Mortgage Default Crisis, Advances in Economics and Business, Vol. 2, Issue 2, pp. 112–119.

A.G. Anderson, (2013), Ambiguity in Securitization Markets, Cornell University Johnson School Research Paper Series, Nr. 5-2013.

A. Schleifer and R.W. Vishny, (2010), Asset Fire Sales and Credit Easing, American Economic Review: Papers & Proceedings, Vol. 100, Issue 2, pp. 46–50. Credit easing has often been used to successfully reduce stress in financial markets, thus avoiding larger output losses. The question that emerged was whether credit easing is also a viable policy tool to cope with banking crises. Jácome et al. conclude that credit easing leads to a sharp increase in domestic currency depreciation, high inflation and a substantial reduction in economic growth in a large panel of emerging and developing economies. For advanced economies, they find the effects to be benign. Credit easing with a view toward reducing systemic stress during a banking crisis doesn’t seem to be a walk in the park and may fuel adverse macroeconomic repercussions. See in detail: L.I. Jácome et al., (2018), Is Credit Easing Viable in Emerging and Developing Economies? An Empirical Approach, IMF Working Paper Nr. WP/18/43. Also: R. Barnichon et al., (2016), Theory Ahead of Measurement? Assessing the Nonlinear Effects of Credit Market Shocks, CEPR Discussion Papers Nr. 11410.

Also in the US as generally understood: A.J. Levitin, A.D. Pavlov and S.M. Wachter, (2009), Securitization: Cause or Remedy of the Financial Crisis? Georgetown Law and Economics Research Paper, Nr. 1462895.

There is, EU wide, a material subsidization of home ownership that should not be underestimated in terms of its impact on housing prices. Fatica and Prammer conclude that tax benefits to homeowners reduce the user cost of housing capital by almost 40% compared to the efficient level under neutral taxation. The bulk of the subsidies stems from under-taxation of the return to home equity, while the average contribution of the tax rebate for mortgage interest payments is driven down by relatively low loan-to-value ratios applied. In detail: S. Fatica and D. Prammer, (2017), Housing and the tax Sytem: How large are the Distortions in the Euro Area? ECB Working Paper Nr. 2087, July 12.

Besides the typical demand-supply relationship, the liquidity availability (and the innovative product design it triggered) was considered the major driver behind the housing bubble in The Netherlands. See the official reporting: Tweede kamer der Staten-Generaal, Parlementair onderzoek ‘Huizenprijzen’, Eindrapport van de tijdelijke commisie huizenprijzen, En reconstructive van twintig jaar stijgende huizenprijzen, Tweede Kamer, vergaderjaar 2012–2013, 33194, nr. 3, pp. 7–8.

In the period 2017–2019 a similar phenomenon occurred in that market. The Dutch Central Bank reported in their most Recent Financial Stability Report that materially elevated housing prices and global debt levels are the two dominant themes in terms of financial stability issues; see DNB, (2019), Overzicht Financiële stabiliteit (Financial Stability Report), Spring edition, June 5, via dnb.nl.

See in detail: D.O. Beltran and C.P. Thomas, (2010), Could Asymmetric Information Alone Have Caused the Collapse of Private-Label Securitization?, International Finance Discussion Papers, FED US.

A. Fostel and J. Geneakoplos, (2011), Tranching, CDS and Asset Prices: Bubbles and Crashes, Princeton Working Paper; J.C. Stein, (2010), Securitization, Shadow Banking and Financial Fragility, Harvard University Working Paper; S. Liu et al., (2017), What Drives Systemic Credit Risk? Evidence from the US State CDS Market, Working Paper, mimeo.

A.J. Levitin and S.M. Wachter (2012), Explaining the Housing Bubble, Georgetown Law Journal, Vol. 100, Issue 4, pp. 1177–1258; M.N. Baily, R.E. Litan, and M.S. Johnson, (2008), The Origins of the Crisis, Fixing Finance Series, Nr. 3, Brookings Institute; M. Aalbers, (2017), The Financialization of Housing: A Political Economy Approach, Abingdon. In recent years similar events are repeating themselves. See in detail: T. Blackwell, and S. Kohl, (2017), Varieties of Housing Finance in Historical Perspective: The Impact of Mortgage Finance Systems on Urban Structures and Homeownership. MPIfG Discussion Paper Nr. 17/2, Köln, Max-Planck-Institut für Gesellschaftsforschung; S. Kohl, (2018), More Mortgages, More Homes? The Effect of Housing Financialization on Homeownership in Historical Perspective, Politics & Society, published online January 27; H. Joebges et al., (2015), What Causes Housing Bubbles? A Theoretical and Empirical Inquiry, Berlin Working Paper on Money, Finance, Trade and Development Nr. 1, November. As was discussed previously the housing market has recovered and the Trump administration announced it considers privatizing the two GSEs (Fannie Mae and Freddie Mac) as a good idea. So housing markets have recovered but there is equal consensus that risks remain (see: Housing and Mortgage Markets May Have Recovered, but Risks Remain, 2018, September 11, Knowledge@Wharton via wharton.​upenn.​edu). The problem is not the reform of the GSEs (insofar as they really took place already), but that ‘broader changes need to be made across the entire mortgage landscape to stabilize the system, even before the final state of the GSEs is fully determined’. See in detail: R. Koss, (2017), Stabilizing the System of Mortgage Finance in the United States, IMF Working Paper Nr. WP/17/186, August. See footnote 48 p. 27 and reference list (pp. 30 ff) for proposals released so far to reform the GSE model and housing finance market. Koss however indicates that it is not clear if any of the systematic proposals offer an appropriate path forward (p. 27) as ‘[m]any of them are based on the idea of multiple entities for which the government would provide catastrophic insurance for securities, but not the debt of the entities themselves. The fatal flaw of these proposals is that there are increasing returns to scale in underwriting and securitization, so it is quite likely that the result would be rapid consolidation…’. He concludes in sadness when pointing out that ‘it may be that “GSE reform” is so buried in ancient culture wars that a new approach is needed altogether.’ One of the lessons to be learned but often forgotten is that complexity and lack of transparency create an environment for opportunistic behavior by private agents that can have serious spillover effects.

A. Buraschi and C. Tebaldi, (2017), Asset Pricing in Network Economies with Systemic Risk, Working Paper, version November 10. See also: D. Acemoglu, et al., (2017), Microeconomic Origins of Macroeconomic Tail Risks, American Economic Review, Vol. 107, Issue 1, pp. 54–108; S. Azizpour, et al., (2018), Exploring the Sources of Default Clustering, Stanford Working Paper, November 15; Journal of financial economics, Vol. 129, Issue 1, pp. 154–183; A. Barattieri, (2016), Banks Interconnectivity and Leverage, CEPR Discussion Papers 11502; B. Herskovic, (2018), Networks in Production: Asset Pricing Implications, Journal of Finance, online edition, April 16.

How that relationship works mathematically is still largely undefined. Nevertheless, the causality has been repeatedly proven. See in detail regarding the relationship between liquidity and real estate prices: A. Cesa-Biachi et al., (2015), Global Liquidity, House Prices, and the Macroeconomy: Evidence from Advanced and Emerging Economies, IMF Working Paper, Nr. WP/15/23. What has also been proven is that capital flows when being synchronized tend to create co-moving real estate prices. A good case study has been the financial integration of the US banking sector in the 80/90’s. They resulted in synchronized housing prices and co-movements across the different US states. It implicitly also proves that free or freeing capital flows has material contagion (due to idiosyncratic risks) effects across regions and between capital market movements and the emergence of contagion. In more recent times, synchronization of housing prices has been fueled by another capital market movement, that is, securitization. Demand or price shocks on the securitization market affect directly the lending ability of agents. Mortgage lending then becomes dependent on the conditions in the securitization market. See in detail: A. Landier et al., (2017), Banking Integration and House Price Comovement, ESRB Working Paper Nr. 48, June.

K. Allen, (2018), No, the Housing Crisis Will Not Be Solved By Building More Homes, Financial Times Alphaville, October 15.

The fact that we have witnessed an avalanche of (new) real estate brokers in recent decades, up till today, helps as well. If you put a barber at every corner of the street, we’ll all end up bald.

It is therefore very unconvincing that the ECB chair Draghi felt the need to reply with the statement that QE is also good for normal people as the family home makes up a large part of family wealth. Due to QE house prices have risen throughout Europe in recent years. The problem is, it is a latent equity that only matters once liquidated. Very few people feel the need to liquidate their homes just to get their hands on the capital gain. As they need to buy anew the capital gain is lot and handed for free to the new seller of the home they are buying. Banks who allow to re-up the mortgage loan to consume the equity built up just keep the circus going. See also on the various elements: J. Ryan-Collins, (2018), Blame the Bank: How Finance Caused Today’s Housing Crisis, October 18, via cityam.​com. The stricter mortgage regulation post-crisis was offset by QE, which did not only create additional liquidity but also drove down rates. Because of the latter not only banks but also non-FI players entered the field. Other studies analyzed the existence of ‘wealth effects’ derived from net equity (in the form of housing, financial assets and total net worth) on consumption. In general terms, wealth effects are found to be relatively large and significant for housing wealth, but less so for other types of wealth, including stocks. Also, the analysis shows how these estimated marginal propensities to consume (MPC) from wealth are closely linked to household characteristics, including income and demographic factors. See in detail: C. Caceres, (2019), Analyzing the Effects of Financial and Housing Wealth on Consumption using Micro Data, IMF Working Paper Nr. WP/19/115, May; also: K. Ji et al. (2019), Disentangling the Effect of Household Debt on Consumption, CPB Discussion Paper, April, The Hague. The latter estimate the contemporaneous relationship between household debt and consumption (the Netherlands). They observe that the average consumption of households with high debt has decreased much more during the crisis than that of other households. They disentangle this into an effect through the availability of credit for direct consumption and an effect through household debt overhang. On the micro level, the consumption drop is the sharpest for the households who are less able or willing to finance one-off high consumption with new debts after the crisis. On the macro level, however, the drop in consumption of households who have negative home equity for a longer period had a much bigger impact on macro consumption, because their number sharply increased during the crisis. Precautionary savings motives among the highly indebted households contributed most to the consumption decline during the crisis.

See for an analysis how different choices and policies yield different results in both advanced and emerging economies: C. banti and K. Phylaktis, (2017), Global Liquidity, House Prices and Policy Responses, Working Paper, February 14, mimeo.

A. Zabai, (2017), Household Debt: Recent Development and Challenges, in BIS Quarterly Review, Q$, December, pp. 39–54.

Even though that can mean that household debt can boost consumption in the short run given the real estate-related payments that need to be made.

Also: A. Alter et al., (2018), Understanding the Macro-Financial Effects of Household Debt: A Global Perspective, IMF Working Paper Nr. WP/18/76, April. They also confirm the negative relationship between household debt and future GDP growth. Three mutually reinforcing mechanisms explain this relationship: (1) debt overhang impairs household consumption when negative shocks hit (also for the inverse relationship: L. Zhang, (2019), Do House Prices Matter for Household Consumption, CPB Discussion Paper, April); (2) increases in household debt heighten the probability of future banking crises, which significantly disrupts financial intermediation; (3) crash risk may be systematically neglected due to investors’ overoptimistic expectations associated with household debt booms. Flexible exchange rates, higher financial development and inclusion are found to mitigate the described impact. Further and concluding in the same direction: A. Mian and E. Verner, (2017), Household Debt and Business Cycles Worldwide, Quarterly Journal of Economics, pp. 1–63; S. Albanesi et al., (2017), Credit Growth and the Financial Crisis: A New Narrative, NBER Working Paper Nr. 23740, National Bureau of Economic Research, Cambridge, MA; J.L. Arcand, (2015), Too Much Finance? Journal of Economic Growth, Vol. 20, Issue 2, pp. 105–148; M. Baron and W. Xiong, (2017), Credit Expansion and Neglected Crash Risk, Quarterly Journal of Economics, pp. 713–764; M. Drehmann et al., (2017), Accounting for Debt Service: The Painful Legacy of Credit Booms, BIS Working Paper Nr. 645.

Household debt is on the rise globally. See for a US analysis: A. Haughwout et al., (2019), Trends in Household Debt and Credit, FRB of NY Staff Report Nr. 882, April.

O. Coibion et al., (2017), Does Greater Inequality Lead to More Household Borrowing? New Evidence from Household Data, Federal Reserve Bank of Minneapolis Working Paper Nr. 17-04, Minneapolis, MN. The hypothesis behind the analysis is that low-income households increased their demand for credit to finance higher consumption expenditures in order to ‘keep up’ with higher-income households. They concluded that ‘low-income households in high-inequality regions accumulated less debt relative to income than their counterparts in lower-inequality regions’.

M. Lombardi et al., (2017), The Real Effects of Household Debt in the Short and the Long Run, BIS Working Papers Nr. 607, January. Cross-country differences can often be explained away by referring to the different degree of legal protection (full recourse vs. non-recourse) of creditors as a variable in the analysis (pp. 19 ff). They conclude on that specific matter that ‘household borrowers’ actual debt service burden is higher for countries with stronger creditor protection than those with weaker creditor protection to the extent that lower loan spreads due to stronger creditor protection do not fully offset higher debt service burden, and thus household consumption and GDP growth is more likely to be lower for these countries’ (p. 23). See also footnote 19 for an overview of supporting literature.

Also: J. Nakajima, (2018), The Role of Household Debt Heterogeneity on Consumption: Evidence from Japanese Household Data, BIS Working Paper Nr. 736, July 24, via bis.​org. Japanese households tend to save more when they have a higher level of debt. In particular, they are sensitive to unexpected declines in income. These results are consistent with findings for other countries. Savings are driven by anticipated unemployment and later stages of life. Reducing sensitivity is possible through a funded pension model and health insurance system.

See also: A. Chudik, et al., (2017), Is There a Debt-Threshold Effect on Output Growth?, Review of Economics and Statistics, Vol. 99, Issue 1, pp. 135–150.

V. Meursault, (2017), Bank Credit Supply and Shadow Mortgage Growth, Working Paper, October 15, mimeo.

L. Zhang et al., (2017), Did Pre-Crisis Mortgage Lending Limit Post-Crisis Corporate Lending? Evidence from UK Bank Balance Sheets, Bank of England Working Paper Nr. 651, March. They conclude ‘[b]anks having more mortgages to borrowers with impaired credit history, or more mortgages to the self-employed, or mortgages with higher loan to value ratios prior to the crisis reduced their lending to non-financial businesses more’ (pp. 23–25). That seems to be very much in line with the findings of D. Bezemer et al., (2017), The Shift in Bank Credit Allocation: New Data and New Findings, DNB Working Paper Nr. 559, June. In their international study covering 78 jurisdictions they categorize bank lending in four categories: home mortgages, consumer credit, bank loans to non-bank financials and loans to non-financial business. They document key trends including the shift in bank credit allocation away from traditional business lending particularly in advanced economies. They suggest substantial consequences of this ‘debt shift’ for growth, income distribution and macroeconomic resilience. They point at financial deregulation as (one of) the main culprit(s) for this debt shift. The shift was ubiquitous across jurisdictions and can therefore not be traced back to micro-level bank features. The shift away from business loans was also identified here toward mortgages. That seems a general conclusion; see also: D. Bezemer et al., (2016), More Mortgages, Lower Growth? Economic Inquiry, Vol. 54, Issue 1, pp. 652–674; O. Jorda et al., (2016), The Great Mortgaging: Housing Finance, Crises, and Business Cycles, Economic Policy. Vol. 31, Issue 85, pp. 107–152; A. Samarina, and D. Bezemer, (2016), Do Capital Flows Change Domestic Credit Allocation?, Journal of International Money and Finance, Vol. 62(C), pp. 98–121. The shift away from business loans toward real estate is also driven by the effects of the corporate sector transition toward a greater use of intangible assets (with lower collateral values). The amount of C&I (commercial and industrial) loans thereby drops proportionately in the portfolio of business loans. Dell’Ariccia et al. argue that banks do not shrink their balance sheets but rather reallocate their lending capacity away from commercial loans and primarily toward real estate and liquid assets. That seems to be in line with A. Caggese and A. Perez, (2017), Capital Misallocation and Secular Stagnation, Finance and Economics Discussion Series Nr. 2017-009, Board of Governors of the Federal Reserve System (U.S.).Also: G. Beningo and L. Fornaro, (2017), Stagnation Traps, ECB Working Paper 2038, March. See regarding the dynamics of resource misallocations during and after the great recession: T. Libert, (2017), Misallocation Before, during and After the Great Recession, Banque de France Working Paper Nr. 658, December 29. Thibault provides an exact decomposition of allocational inefficiency into three components: labor misallocation, capital misallocation and a third term representing the interplay between both. Misallocation increased substantially between 1997 and 2007. The main feature behind the rise in misallocation during the crisis is the predominance of the interplay component. It suggests that one should pay special attention to mechanisms disrupting both labor and capital markets in the wake of financial crises. Finally, allocational efficiency remains rather constant after 2010. Also: F. Schivardi et al., (2017), Credit Misallocation during the European Financial Crisis, Bank of Italy Working Paper Nr. 1139, September 27. They find that during the Eurozone financial crisis (i) undercapitalized banks were less likely to cut credit to non-viable firms; (ii) credit misallocation increased the failure rate of healthy firms and reduced the failure rate of non-viable firms; and (iii) nevertheless, the adverse effects of credit misallocation on the growth rate of healthier firms were negligible.

D. Bezemer, et al., (2016), More Mortgages, Lower Growth? Economic Inquiry, Vol. 54, Issue 1, pp. 652–674. Also: S. Bahaj, et al., (2016), The Residential Collateral Channel, Centre for Macroeconomics Discussion Paper, Nr. 2016-07; O. Jordà, et al., (2016), The Great Mortgaging: Housing Finance, Crises, and Business Cycles. Economic Policy, pp. 107–152.

W. Lian, (2019), Fundamental and Speculative Demands for Housing, IMF Working Paper Nr. WP/19/63, March.

R. Dieci and F. Westerhoff, (2009), A Simple Model of a Speculative Housing Market, Working Paper Nr. 62 uni. Bamberg, February, mimeo via uni-bamberg.de.

G. Kaplan et al., (2017), The Housing Boom and Bust: Model Meets Evidence, National Bureau of Economic Research, Working Paper Nr. 23694. Also as Kreisman Working Paper Series in Housing Law and Policy 2017/51, University of Chicago. Also: N. Barberis, et al. (2018), Extrapolation and Bubbles, Journal of Financial Economics, Vol. 129, Issue 2, pp. 203–227. See also for the moral hazard with mortgage insurers in the run up to the great recession (expanded insurance on high-risk mortgages at the tail-end of the housing boom, contradicting the industry’s own research regarding house price risk). In detail: N. Bhutta and B.J. Keys, (2018), Eye Wide Shut? The Moral Hazard of Mortgage Insurers during the Housing Boom, NBER Working Paper Nr. 24844, July.

G. Sutton et al., (2017), Interest Rates and House Prices in the United States and Around the World, BISWorking Paper Nr. 665, October 13, via bis.​org. They find a surprisingly important role for short-term interest rates as drivers of house prices, especially outside the US. In addition, they find a strong link between US interest rates and changes in house prices in other countries. Studies that treat housing as a form of investment whose alternative is renting property predict much higher interest rate sensitivity of house prices than those we obtain.

W. Lian, (2019), Fundamental and Speculative Demands for Housing, IMF Working Paper Nr. WP/19/63, March, p. 1.

A. Mian and A. Sufi, (2019), Credit Supply and Housing Speculation, NBER Working Paper Nr. 24823, April (revised edition). They conclude ‘[s]peculation is a critical channel through which credit supply expansion affects the housing cycle. The surge in private label mortgage securitization in 2003 fueled a large expansion in mortgage credit supply by lenders financed with noncore deposits. Areas more exposed to these lenders experienced a large relative rise in transaction volume driven by a small group of speculators, and these areas simultaneously witnessed an amplified housing boom and bust’. Cox and Lugvigson discuss the two driving forces of house price fluctuations: credit conditions and beliefs. Changes in credit conditions are positively related to the fraction of riskier non-conforming debt in total mortgage lending. Credit conditions explain quantitatively large magnitudes of the variation in quarterly house price growth and also predict future house price growth. Beliefs bear some relation to contemporaneous house price growth but have little predictive power. In detail: J. Cox and S.C. Lugvigson et al., (2019), Drivers of the Great Housing Boom-Bust: Credit Conditions, Beliefs, or Both? NBER Working Paper Nr. 25285, January, Revised.

Recently: Z. Gao et al., (2018), Economic Consequences of Housing Speculation, Working paper, mimeo, December, including a massive literature list with supporting documentation (pp. 28–31). Regarding the role of credit policy, see: M. Pidkuyko, (2019), Heterogeneous Spillovers of Housing Credit Policy, University of Manchester Working Paper, mimeo, February.

There is still no consensus however on how to determine the user-cost-of-housing at an aggregate level. Suggestions are CPI rent, but that has its own challenges. Lian suggests as an alternative model ‘to construct user-cost-of-housing as the difference between house price today and the discounted value of expected house price in the next period’ (p. 2). He immediately admits that the challenge here is that there is no reliable measurement of house price expectations at a national level. He circumvents that problem by using the supply in the market for housing services as a proxy. He argues ‘[u]nder the assumption that the market for housing services is under perfect competition, the price of housing services (user-cost-of-housing) is the same as the cost of producing housing services, which further depends on housing services production function and input costs’ (p. 2, also for further details on how he constructs the (de)nominator).

By including this factor he avoids capturing conventional business cycle shocks.

W. Lian, (2019), Fundamental and Speculative Demands for Housing, IMF Working Paper Nr. WP/19/63, March, pp. 3–4 for his findings. Also: C. Burnside, et al., (2016), Understanding Booms and Busts in Housing Markets, Journal of Political Economy, Vol. 124, Issue 4, pp. 1088–1147; E. Glaeser, et al., (2018), The Economic Implications of Housing Supply, Journal of Economic Perspectives, Vol. 32, Issue 1, pp. 3–30; R. Greenwood et al., (2018), Bubbles for Fama, Journal of Financial Economics, https://​doi.​org/​10.​1016/​j.​jfineco.​2018.​09.​002. Davis and Haltiwanger highlight that housing markets, credit conditions and boom-bust cycles impact young firm survival and performance. See in detail: S.J. Davis and J. C. Haltiwanger, (2019), Dynamism Diminished: The Role of Housing markets and Credit Conditions, NBER Working Paper Nr. 25466, January. Alo: B. Őzturk et al., (2018), The Relation between Supply Constraints and House Price Dynamics in The Netherlands, DNB Working Paper Nr. 601, July 18. In particular, the latter look at whether income shocks lead to stronger house price increases in regions characterized with higher supply constraints. Their results suggest that income shocks lead to significantly larger increases in house prices in municipalities that are relatively more supply constrained.

Prices are often driven by the dynamics of appraisals by realtors. There is a systemic dimension to appraiser valuation and pricing in real estate markets. See, for instance, in the Netherlands where the central bank concluded that the quality, reliability and independence of appraiser reporting are poor: R. van der Molen and R. Nijskens, (2019), De kwaliteit en onafhankelijkheid van woningtaxaties, DNB Occasional Studies, Amsterdam.

See M. Andrle and M. Plašil, (2019), Assessing House Prices with Prudential and Valuation Measures, IMF Working Paper Nr. WP/19/59, March. See Chap. 2 for the borrowing capacity approach (pp. 6–13) and the more familiar intrinsic value approach in chapter three (pp. 14–19). They applied both methods to value real estate in the Czech Republic and the level of over- or undervaluation. See also: N. Geng, (2018), Fundamental Drivers of House Prices in Advanced Economies, Working Paper Nr. WP/18/164, International Monetary Fund, Washington DC and N. Philiponnet, and A. Turrini, (2017), Assessing House Price Developments in the EU, Discussion Paper Nr. 048/2017, European Commission, Brussels.

‘Excess’ refers here to levels elevated above those normally warranted given the level of economic activity and interest rates in the market at any given point in time.

Y. Amihud et al., (2005), Liquidity and Asset Prices, Foundations in Trends and Finance, Vol. 1, Nr. 4, pp. 269–364. Also: J. Tripathy, (2017), Bubble Equilibria with Credit Misallocation, Bank of England Working Paper Nr. 649, February. It was concluded that asset bubbles raise interest rates and lower investment productivity by directing financial resources to the sector with lower financial constraints. Such states are coined ‘bubbly growth traps’. In such a state, ‘asset bubbles not only crowd-out investments, but also absorb valuable financial resources that (if used in real economic activity) provide the wealth essential to initiate financially constrained, productive projects in the future’… ‘[t]The loss of financial resources to asset bubbles can rule out the transition to the high investment productivity phase when the economy never acquires the wealth necessary to make the said transition.’ Also: A. Martin and J. Ventura, (2016), Managing Credit Bubbles. Journal of the European Economic Association, Vol. 14, Issue 3, pp. 753–789, June.

A. Bruggeman, (2007), Can Excess Liquidity Signal an Asset Price Boom?, NBB Working Paper, Nr. 117; B. Bierut, (2013), Global Liquidity as an Early Indicator of Asset Price Booms, DNB Working Paper, Nr. 377. It also leads to excessive private debt, see: M. Jarmuzek and R. Rozenov, (2017), Excessive Private Sector Leverage and Its Drivers: Evidence from Advanced Economies, IMF Working Paper Nr. WP/17/72, March, in particular pp. 14–17. Excessive private debt also triggers collateral implications. It was observed that monetary policy is more effective as private household debt increases. Although the responsiveness of household consumption has diminished since the crisis, household balance sheets are not the culprit. The factor that can explain reduced responsiveness to monetary policy is overall economic uncertainty (since the financial crisis of 2008). See in detail: G. Gelos et al., (2019), Has Higher Household Indebtedness Weakened Monetary Policy Transmission?, IMF Working Paper Nr. WP/19/11, January.

P. Hörhdahl and F. Packer, (2006), Understanding Asset Prices An Overview, BIS Working Papers Nr. 34.

Through tranching; see in detail: A. Antoniades and A. Tarashev, (2014), Securitizations: Tranching Concentrates Uncertainty, BIS Quarterly Review December, pp. 37–53. The new December 2014 issued securitization standards contribute very little to solving this problem; see in extenso: BIS, (2014), Revisions to the Securitizations Framework, Basel III Document.

Banks tend to extract informational rents through collateral use. The rent extraction declines post-IPO and for lower-risk firms. See: B. Xu et al., (2016), Do Banks Extract Informational Rents through Collateral, Hong Kong Institute for Monetary Research Working Paper Nr. 1, January. The rent extraction of shadow banks follows the fact that banks reduced lending to risky lenders. The leveraged lending guidance of banks, which encouraged banks to reduce lending to leveraged borrowers, had an adverse effect on the profitability and investment levels of the affected firms, that is, the lenders of last resort, non-banks extract rents from borrowers with limited access to external finance. In detail: S. Biswas et al., (2019), Non-Bank Loans, Corporate Investment, and Firm Performance, Working Paper February 1, mimeo.

S. Kohl, (2018), More Mortgages, More Homes? The Effect of Housing Financialization on Homeownership in Historical Perspective, Politics and Society, Vol. 46, Nr. 2, pp. 177–203.

J. Hessel and J. Peeters, (2011), Housing Bubbles, the Leveraging Cycle and Role of Central Banking, DNB Occasional Studies, Vol. 9, Nr. 5; A. Nocera and M. Roma, (2017), House Prices and Monetary Policy in the Euro Area: Evidence from Structural VARs, ECB Working Paper Nr. 2073, June; A. Martin et al., (2018), The Financial Transmission of Housing Bubbles: Evidence from Spain, bank of Spain Working Paper Nr. 23. They show that if firms and banks face collateral constraints, a housing bubble initially raises credit demand by housing firms while leaving credit supply unaffected. It therefore crowds out credit to non-housing firms. If time passes and the bubble lasts, however, housing firms eventually pay back their higher loans. This leads to an increase in banks’ net worth and thus to an expansion in their supply of credit to all firms: crowding-out gives way to crowding-in.

J. Huang and J. Wang, (2010), Market Liquidity, Asset Prices and Welfare, Journal of Financial Economics, Vol. 95, Issue 1, pp. 107–127, previously released as MIT Working Paper (2008) and NBER Working Paper, Nr. 14058 (2008).

L. Arrondel et al., (2014), Wealth and Income in the Euro Area. Heterogeneity in Households’ Behaviours, ECB Working Paper Series, Nr. 1709. Mortgage debt is a key contributor to high household indebtedness, which tends to be a key vulnerability to the financial sector. Higher LTV ratios tend to occur in places with strong house price growth. In technical terms, these mortgages have a higher LTI-ratio than on average as well as longer maturities. Mutatis mutandis households with mortgages that have these two characteristics are more vulnerable in the event of a major adverse shock to household income. These trends are more pronounced among younger households. They are also concentrated in regions with imbalances in the housing market. See, for instance, for a Canadian study on the matter: O. Bilyk et al., (2017), Analysis of Household vulnerabilities Using Loan-Level Mortgage Data, Bank of Canada, Financial System Review, November, pp. 21–33.

One would think that the financial crisis provided sufficient insights into the matter for it not to happen again. However, a decade after the financial crisis we are drawing the same conclusions all over again. See, for instance, D. Olick, (2018), Run-Up in Home Prices is ‘Not Sustainable’: Realtors’ Chief Economist, cnbc.​com, May 30, update.

To put it differently: Asset bubbles in assets that have a widespread utility function have regressive welfare effects. By raising the housing price, the bubble benefits high-income savers but negatively affects low-income borrowers, claim Graczyck and Phan. By creating a bubble in the market price, ‘savers’ demand for the housing asset for investment purposes imposes a negative externality on borrowers, who only demand the housing asset for utility purposes’. There is also a feedback loop: ‘high income inequality depresses the interest rates, facilitating the existence of housing bubbles, which in turn have regressive welfare effects’. The key aspect here is that ‘by creating a bubble in the market price of housing, savers’ demand for the housing asset for investment purposes imposes a negative pecuniary externality on borrowers, who only demand the housing asset for utility purposes’ (p. 3). See in detail: A. Graczyck and T. Phan, (2018), Regressive Welfare Effects of Housing Bubbles, Federal Reserve Bank of Richmond Working Paper Nr. WP18-10, April 13. What is particularly interesting in this study is the fact that they distinguish between buyers with savings and those that need to borrow to acquire a home (that has a satisfiable utility function to both). Those buyers with liquidity will acquire housing in excess of the satiation level, because they would like to use the housing asset as an investment vehicle to save for old age (pp. 10 ff). By extension this category also includes buyers who buy-to-let as thus as a 100% investment vehicle. Housing bubbles have opposite effects on borrowers and savers. They highlight: ‘[t]he housing bubble increases the return from real estate investment for high-income savers, who demand storage of value, and hence increases their welfare (relative to the bubbleless benchmark). In contrast, the housing bubble reduces the welfare of borrowers, because it raises the price of housing and the speculative demand of savers for the housing asset crowds out the allocation of housing to borrowers, who in equilibrium have a relatively higher marginal utility from housing’ (pp. 2–3). By positively affecting high-income savers and negatively affecting low-income borrowers, the housing bubble thus has regressive welfare effects. The crowding effect on housing that was prevalent in the housing bubble case is absent in the presence of a pure bubble (p. 4). The logic behind it is simple. In a pure bubble, the price of an asset without a fundamental value inflates and constitutes an investment opportunity for savers. In such a pure bubble, an endogenous segmentation exists, because only savers purchase a bubble asset for investment purposes (although one can imagine non-savers could borrow to do so as well). Credit-constrained borrowers have no demand for the asset (besides the aforementioned consideration) and thus no crowding out occurs. No negative externality is put on borrowers in such cases and this is in contrast to the housing market. Also: E. Farhi, and J. Tirole, (2012), Bubbly liquidity. The Review of Economic Studies, Vol, 79, Issue 2, pp. 678–706; S. Giglio, et al., (2016), No-Bubble Condition: Model-Free Tests in Housing Markets, Econometrica, Vol. 84, Issue 3, pp. 1047–1091; T. Hirano, and N. Yanagawa, (2017), Asset Bubbles, Endogenous Growth, and Financial Frictions, Review of Economic Studies, Vol. 84, pp. 406–443; D. Ikeda, and T. Phan, (2016), Toxic Asset Bubbles, Economic Theory, Vol. 61, Issue 2, pp. 241–271; B. Zhao, (2015), Rational Housing Bubble, Economic Theory, Vol. 60, Issue 1, pp. 141–201.

M. Wolf, (2014), Deeper Reform of Housing Finance is Vital for Stability, Financial Times, September 18.

Between 2003 and 2006, the Federal Reserve raised rates by 4.25%. Yet it was precisely during this period that the housing boom accelerated, fueled by rapid growth in mortgage lending. There is deep disagreement about how, or even if, monetary policy impacted the boom. Using heterogeneity in banks’ exposures to the deposits channel of monetary policy, Drechsler et al. show that Fed tightening induced a large reduction in banks’ deposit funding, leading them to contract new on-balance-sheet lending for home purchases by 26%. However, an unprecedented expansion in privately securitized loans, led by non-banks, largely offset this contraction. Since privately securitized loans are neither GSE-insured nor deposit-funded, they are run-prone, which made the mortgage market fragile. See in detail: I. Drechsler et al., (2019), How Monetary Policy Shaped the Housing Boom, NBER Working Paper 25649, March. The more recent re-emergence of privately securitized mortgages has closely tracked the recent increase in rates. When the share of mortgage lending during the great recession slowed at larger banks, shadow banks and fintech lenders stepped in. However, that did not prevent the more relevant role that small banks started to play in this field. Small banks were twice as responsive as shadow banks to fill the gap left by the big banks’ retreat, and more than four times more responsive than fintech lenders. Small banks are key in the economy despite the rise in shadow banking and the ascent of fintech business models. In detail: T.A. Begley and K. Srinivasan, (2019), Northeaster U. D’Amore-McKim School of Business Research Paper Nr. 3317672, May 21.

Benes et al. evidence how household demand for housing, house prices and bank mortgages are intertwined in what we call a deadly embrace. Without macroprudential policies, this deadly embrace naturally leads to housing boom and bust cycles, which can be very costly for the economy. See in detail: J. Benes et al., (2016), Mitigating the Deadly Embrace in Financial Cycles: Countercyclical Buffers and Loan-to-Value Limits, IMF Working Paper Nr. 16/87, April.

See in detail: A. Justiniano et al., (2015), Credit Supply and the Housing Cycle, Federal Reserve Bank of NY, Staff Nr. 709, February. Also observe the attractive reading list on pp. 37–42 on the matter of credit supply and (housing) booms.

E. Cerutti et al., (2015), Housing Finance and Real-Estate Booms: A Cross-Country Perspective, IMF Staff Discussion Note, June, SDN/15/12.

See for a historical perspective on housing finance: T. Blackwell and S. Kohl, (2017), Varieties of Housing Finance in Historical Perspective. The Impact of Mortgage Finance Systems on Urban Structures and Homeownership, MPIfG Discussion Paper 17/2, Max-Planck-Institut für Gesellschaftsforschung, Köln, February.

Many studies highlight the strong link that exists between housing prices and credit. Often there is a full synchronization between the credit cycle and housing prices. See, for instance, I. Ioannou, (2018), Housing Price, Credit, and Output Cycles: How Domestic and External Shocks Impact Lithuania’s Credit, IMF Working Paper Nr. WP/18/160, July; E. Dermani et al., (2016), Is a Bubble Forming in Swedish Housing Prices? Sveriges Riksbank Economic Review pp. 2 ff; N. Geng, (2017), Are House Prices Overvalued in Norway?—A Cross-Country Analysis, Norway 2017 Selected Issues, IMF Country Report Nr. WP/17/181; Z. Smidova, (2016), Betting the House in Denmark, OECD Economics Department Working Paper Nr. 1337.

Ungerer documents that expansionary monetary policy leads to increased household leverage and housing sales rates. Both the housing sales rate and loan-to-value ratios increase after expansionary monetary policy. The interaction between credit frictions and housing market search frictions generates endogenous movements in the loan-to-value ratio, which amplify the economy’s response to monetary policy; see: C. Ungerer, (2015), Monetary Policy, Hot Housing Markets and Leverage, Finance and Economics Discussion Series Divisions of Research & Statistics and Monetary Affairs Federal Reserve Board, Washington, D.C., Working Paper Nr. 2015-048, May 22.

D.K.G. de Araujo et al., (2018), Loan-to-Value Policy and Housing Finance: Effects on Constrained Borrowers, BIS Working Paper Nr. 673, November 17, via bis.​org. The objective of LTV limits for housing loans is to increase borrower resilience and to lower bank losses during downturns. Imposing LTV limits shifts several characteristics of the loan contract and influences borrowers’ behavior. In all segments of housing finance, constrained borrowers end up meeting the new LTV limit. However, the impact on middle-income borrowers is greater. In this segment, borrowers affected by the new regulation purchase more affordable houses.

See for a write-up of the FED’s framework for implementing monetary policy prior to the expansion of its balance sheet during the financial crisis: A. Kroeger et al., (2017), The Pre-Crisis Monetary Policy Implementation Framework, Federal Reserve Bank of New York Staff Reports, Nr. 809, March.

See about the globalizing nature of inflation (and its limits): R. Bems et al., (2018), Is Inflation Domestic or Global. Evidence from Emerging Markets, IMF Working Paper Nr. WP/18/241, November.

Turk argues that ‘given the rollback of Dodd-Frank that is currently being contemplated within Congress and the Trump administration will further marginalize the post-crisis rulemakings and reinforce the centrality of regulation by settlement’ in particular in areas such as securitization; see: M.C. Turk, (2018), Securitization Reform After the Crisis: Regulation by Rulemaking or Regulation by Settlement?, Review of Banking and Financial Law, Vol. 37, pp. 870–874.

D. Schwarcz and D. Zaring, (2017), Regulation by Threat: Dodd-Frank and the Nonbank Problem, The University of Chicago Law Review, Vol. 84, pp. 1813–1881. About the relationship between ‘too big to fail’, moral hazard, bailouts and corporate responsibility: S.L. Schwarcz, (2017), Too Big to Fool: Moral Hazard, Bailouts, and Corporate Responsibility, Minnesota Law Review, Vol. 102, pp. 761–801. Schwarcz argues that the too-big-to-fail problem is exaggerated, but that moral hazard ‘causes systemically important firms to engage in excessive risk-taking. Such risk-taking is more likely to be caused by other factors, including a legally embedded conflict between corporate governance and the public interest that allows managers of those firms to ignore systemic externalities’. This is a pure legal problem that can and should be addressed by making managers account for systemic externalities.

R. Bubb and P. Krishnamurthy, (2015), Regulating Against Bubbles: How Mortgage Regulation Can Keep Main Street and Wall Street Safe- From Themselves, University of Pennsylvania Law Review, Vol. 163, Nr. 6 (May), pp. 1539–1630.

R. Elul, (2015), Securitization and Mortgage Default, Federal Reserve Bank of Philadelphia Working Paper Nr. 51-15. The global crisis narrative has suggested that an expansion of subprime credit was the reason for rising mortgage defaults, leading to the large-scale recession in 2007–2009. Taking a closer look at the characteristics of subprime credit holders over the period, it is argued that the growth in mortgage defaults did not occur predominantly amongst subprime credit holders. Instead, it was real estate investors that played a critical role in the rise in mortgage debt, specifically among the middle and the top of the credit score distribution. See in detail: S. Albanesi et al., (2017), Mortgage Default during the Great Recession Came from Real Estate Investors, not Subprime Credit Holders, October 3, via voxeu.​org

That is economically and policy-wise materially important, as the largest chunk of the mortgage market were prime instruments.

See, for instance, in the case of China: R.N. Lai and R. Van Order, (2016), Shadow Banking and the Property Market in China, Working Paper, March, mimeo. They found that house prices grew ‘more rapidly with availability of shadow banking funds’, which have grown rapidly.

A. Cesa-Bianchi et al., (2015), Global Liquidity, House Prices, and the macro-economy: Evidence from Advanced and Emerging Economies, IMF Working Paper Series, Nr. WP/15/23.

M. Katagiri, (2018), House Price Synchronization and Financial Openness: A Dynamic Factor Model Approach, IMF Working Paper Nr. WP/18/208, September.

The variance explained by the global common factor rose from 10 to 40% during 1970–2016. That in itself is concerning as ‘a risk to macro-financial stability through the housing cycle in one country could be amplified and transmitted to another country, thus possibly causing macro-financial instability on a global scale’ (Ibid., p. 4).

As global portfolio volumes get bigger, so does the tendency to spread capital across countries and regions as a dynamic of diversification. But as the research demonstrates, this behavior leads to synchronization across different housing markets.

Ibid. pp. 4–5.

N. Geng, (2018), Fundamental Drivers of House Prices in Advanced Economies, IMF Working Paper Nr. WP/18/164, July, in particular pp. 7–13.

See regarding the relationship between demography and inflation: M. Juselius and E. Takáts, (2018), The Enduring Link Between Demography and Inflation, BIS Working Paper Nr. 722, May 7. Inflationary pressure rises when the share of dependants increases and, conversely, subsides when the share of working age population increases. This relationship accounts for the bulk of trend inflation. It predicts rising inflation over the coming decades.

Geng claims, and I fully concur, that structural reforms can over time improve housing affordability, thereby reducing debt accumulation and enhancing financial stability. These structural reforms include, but are not limited to, ‘reforms to raise the long-run elasticity of housing supply, phasing out rent control, and reducing tax incentives for home ownership and debt financing. Such instruments may complement and support the more commonly used macroprudential tools such as limits on loan-to-value ratios, as the impact of reforms on supply and demand fundamentals may shape longer-term expectations in the housing market (p. 21)’. See also: A.K. Anundsen and C. Heebøll, (2016), Supply Restrictions, Subprime Lending and Regional US House Prices, Journal of Housing Economics, Vol. 31, pp. 54–72; S. Hviid and A. Kuchler, (2017), Consumption and Savings in A Low Interest-Rate Environment, Working Paper Nr. 116, Danmarks National bank.

A. Alter et al., (2018), House Price Synchronicity, Banking Integration, and Global Financial Conditions, IMF Working Paper Nr. WP/18/250. Also: O. Akinci, and J. Olmstead-Rumsey. 2017. How Effective Are Macroprudential Policies? An Empirical Investigation.” Journal of Financial Intermediation, Vol. 33 (January), pp. 33–57; C. Badarinza, and T. Ramadorai, (2018), Home away from Home? Foreign Demand and London House Prices, Journal of Financial Economics, Vol. 26, pp. 21–34; E. Cerutti et al., (2017), Housing Finance and Real-Estate Booms: A Cross-Country Perspective, Journal of Housing Economics, Vol. 38 (C), pp. 1–13; M. Demirer et al., (2018), Estimating Global Bank Network Connectedness, Journal of Applied Econometrics, Vol. 33, Issue 1, pp. 1–15; IMF, (2018), House Price Synchronization: What Role for Financial Factors? Global Financial Stability Report, April, Washington, DC.

A. Alter et al., (2018), Ibid. p. 26. Also: M. Katagiri, (2018), House Price Synchronization and Financial Openness: A Dynamic Factor Model Approach, IMF Working Paper Nr. WP/18/209, International Monetary Fund, Washington, DC.; Ò Jordà et al., (2017), The Rate of Return on Everything, 1870–2015, NBER Working Paper Nr. 24,112, National Bureau of Economic Research, Cambridge, MA; K.N. Kuttner, and I. Shim, (2016), Can Non-Interest Rate Policies Stabilize Housing Markets? Evidence from a Panel of 57 Economies, Journal of Financial Stability, Vol. 26 (October), pp. 31–44; A. Landin et al., (2017), Banking Integration and House Price Co-movement, Journal of Financial Economics, Vol. 125, Issue 1, pp. 1–25.

J. Ryan-Collins, (2018), Why Can’t You Afford a Home, 1st edition, Polity, Cambridge, UK and K. Allen, (2018), No, the Housing Crisis will not be Solved by Building more homes, FT Alphaville, October 15. See also the documentation for the conference on the same topic at Sustainable Finance Lab Amsterdam, January 15, 2019 as well as K. Haegens, (2019), Econoom Josh Ryan-Collins: ‘Stijgende huurprijzen zijn een sociale én economische ramp’, De Volkskrant, January 16; J. Ryan-Collins et al., (2017), Rethinking the Economics of Land and Housing, Zed Books, London, UK. The demand-supply relationship is less dominant in the price formation than traditionally thought, and credit liquidity a whole lot more. Ryan-Collins refers to markets where supply of real estate grew much faster than the economy and prices still grew disproportionately. But prices did follow tune with credit availability in those markets. You can expand mortgage credit faster in any situation than the effective supply of real estate. Rising real estate prices are a social and economic demon for a number of reasons: real estate is a low value-add industry, it drains liquidity away from industries and activities where the liquidity would be badly needed, expanding mortgage lending by banks is matched with reduced corporate and business lending, and it reduces purchasing power as households and businesses spend more on real estate year-on-year. It was only two days later that the Dutch scientific council for government policy brought out an extensive report demonstrating pretty much the same as well pointing at the overall issues that money creation in effect is out of control and pointing to the largely uncontrolled ability of banks to increase the money/credit supply. See: WRR, (2019), Geld en schuld. De publieke rol van banken, The Hague, January 17. The liberalization of credit markets and removal of credit guidance are significantly associated with a lower share of lending to non-financial firms also, conclude Bezemer et al. See: D. Bezemer et al., (2018), Credit where it’s Due: A Historical, Theoretical and Empirical Review of Credit Guidance Policies in the 20th Century, UCL Institute for Innovation and Public Purpose (IIPP) Working Paper Nr. 2018-11, December, London. Ee also: J. Arcand, et al. (2015), Too Much Finance? Journal of Economic Growth, Vol. 20, Issue 2, pp. 105–148; D. Bezemer, et al. (2016), More Mortgages, Lower Growth? Economic Inquiry, Vol. 54, Issue 1, pp. 652–674; D. Bezemer, et al. (2017), The Shift in Bank Credit Allocation: New Data and New Findings, De Nederlandsche Bank Working Paper Nr. 559, Amsterdam D. Bezemer and L. Zhang, (2019), Credit Composition and the Severity of Post-Crisis Recessions, Journal of Financial Stability, Vol. 42, pp. 52–66; G. Epstein, (2018), On the Social Efficiency of Finance, Development and Change, Vol. 49, Issue 2, pp. 330–352; S. Storm, (2018), Financialization and Economic Development: A Debate on the Social Efficiency of Modern Finance, Development and Change, Vol. 49, Issue 2, pp. 302–329; L. Zhang, (2017), Did Pre-Crisis Mortgage Lending Limit Post-Crisis Corporate Lending? Evidence from UK Bank Balance Sheets, Bank of England Staff Working Paper Nr. 651, London; F. Brever et al. (2018), Scheitern der sozialen Wohnungspolitik: Wie bezahlbaren Wohnraum schaffen? Ifo Schnelldienst Vol. 17, Issue 21, pp. 3–30, Ifo-Institut für Wirtschaftsforschung, via cesinfo-group.de. Anenberg and Kung conclude that the rent elasticity is low, and thus marginal reductions in supply constraints alone are unlikely to meaningfully reduce rent burdens, all pointing at underwriting Ryan-Collins’s argument. See: E. Ananberg and E. Kung, (2018), Can More Housing Supply Solve the Affordability Crisis? Evidence from a Neighborhood Choice Model, Finance and Economics Discussion Series 2018-035. Washington: Board of Governors of the Federal Reserve System, https://​doi.​org/​10.​17016/​FEDS.​2018.​035

IMF, (2019), Global Financial Stability Report, Chapter two: Downside Risk to House Prices, April 4, Washington D.C. Also: B. Algieri, (2013), House Price Determinants: Fundamentals and Underlying Factors, Comparative Economic Studies Nr. 55, pp. 315–341; G. Kaplan et al., (2017), The Housing Boom and Bust: Model Meets Evidence, NBER Working Paper 23694, National Bureau of Economic Research, Cambridge, MA.

Also: J. Favilukis et al., (2017), The Macroeconomic Effects of Housing Wealth, Housing Finance, and Limited Risk Sharing in General Equilibrium, Journal of Political Economy, Vol. 125, Issue 1, pp. 140–223. On the matter of the relation between household debt and house prices see: A. Mian and A. Sufi, (2016), Who Bears the Cost of Recessions? The Role of House Prices and Household Debt, Chapter 5 in Handbook of Macroeconomics, edited by John B. Taylor and Harald Uhlig, Elsevier, Amsterdam. Also on the same matter: J. Cloyne et al., (2017), The Effect of House Prices on Household Borrowing: A New Approach, NBER Working Paper Nr. 23861, September. The latter conclude twofold: First, there is a clear and robust effect of house prices on borrowing, but the responsiveness is smaller than recent US estimates. Second, the effect of house prices on borrowing can be explained largely by collateral effects.

IMF, (2018), Ibid. p. 70, footnote 19: Credit booms are defined as periods during which the credit-to-GDP ratio is above the long-term trend. Credit booms tend to have a direct effect on house price risk is the consequence that the rapid credit expansion correlates with overstretched household balance sheets. Also: G. Favara and J. Imbs, (2015), Credit Supply and the Price of Housing, American Economic Review, Vol. 105, pp. 958–992; B. Bianchi, (2018), Structural Credit Ratios, ESRB Working Paper Nr. 85, October 15. The macroeconomic variables that correlate most with the credit-to-GDP ratio are economic development, the investment share in GDP and inflation.

The model developed builds upon the Growth-at-Risk Model developed by the IMF in their 2017 Global Financial Stability Report. The growth-at-risk approach is a measure for financial stability and links current financial conditions to the distribution of future growth outcomes. See also: T. Adrian et al., (2018), The Term Structure of Growth-at-Risk, IMF Working Paper Nr. WP/18/180, August. They produce a forward-looking analysis, based on historical data.

Also: T. Adrian et al., (2019), Vulnerable Growth, American Economic Review, Vol. 109, Issue 4, pp. 1263–1289. L. Agnello and L. Schuknecht, (2011), Booms and Busts in Housing Markets: Determinants and Implications, Journal of Housing Economics, Vol. 20, Issue 3, pp. 171–190.

See for the role of shadow banks in the housing sector and its implications: H. Desgagnés, (2017), The Rise of Non-Regulated Financial Intermediaries in the Housing Sector and its Macroeconomic Implications, Bank of Canada Staff working Paper Nr. 36, September. Her results suggest that the non-regulated sector contributes to stabilize the economy by providing an alternative source of capital when the regulated sector is unable to fulfill the demand for credit. As a result, an economy with a large non-regulated sector experiences a smaller downturn after an adverse financial shock. Culp and Neves from their part highlight that the economic purpose of shadow banking is to enable commercial banks to raise funds from and transfer risks to non-bank institutions. In that sense, the shadow banking system is a shock absorber for risks that arise within the commercial banking system and are transferred to a more diverse pool of non-bank capital instead of remaining concentrated among commercial banks. Also, they hint at the fact that most recent and incoming regulations could result in a less stable financial system to the extent that higher regulatory costs on shadow banks like insurance companies and asset managers could discourage them from participating in shadow banking. And the net effect of this regulation, by limiting the amount of market-based capital available for non-bank risk transfer, may well be to increase the concentrations of risk in the banking and overall financial system. See: C.L. Culp and A.M.P. Neves, (2017), Shadow Banking, Risk Transfer, and Financial Stability, Journal of Applied Corporate Finance, Vol. 29, Issue 4, pp. 45–64.

T. Poghosyan, (2019), How Effective is Macroprudential Policy? Evidence from Lending Restrictions Measures in EU Countries, IMF Working Paper Nr. WP/19/45, February. Also: K. Kuttner, and I. Shim, (2016), Can Non-Interest Rate Policies Stabilize Housing Markets? Evidence from a Panel of 57 Economies, Journal of Financial Stability, Vol. 26, pp. 31–44; L. Jacome, and S. Mitra, (2015), LTV and DTI Limits—Going Granular, IMF Working Paper Nr. WP/15/154, International Monetary Fund: Washington, D.C; E. Cerutti et al., (2017), Housing Finance and Real-Estate Booms: A Cross–Country Perspective, Journal of Housing Economics, Vol. 38, pp. 1–13.

Used as a baseline for identifying the role of shadow banks in the mortgage industry in: S. Pool, (2018), Mortgage Debt and Shadow Banks, DNB Working Paper Nr. 588, March, Amsterdam. The inflow of deposits leads to more mortgage lending and less corporate lending. As housing stock is fixed (or cannot grow as fast as deposits and thus mortgage lending), the value of the collateral rises rather than the volume of collateral. Increasing levels of mortgage loans only supported by higher collateral values dampens productivity and price boom-bust cycles. In order to reduce the involvement of shadow banks in the mortgage industry he suggests restrictions on admissible loan-to-value ratios for mortgage loans to reduce house price and mortgage supply fluctuation as well as the introduction of interest-paying central bank deposits for households to raise the costs for banks to finance themselves with uninsured deposits. See for model development (pp. 12–28) and findings (pp. 29–35).

S. Hanson, et al., (2015), Banks as Patient Fixed-Income Investors, Journal of Financial Economics, Vol. 117, Issue 3, pp. 449–469.

For those who wonder how that last step works: lower interest rates in the market reduce your net interest margin (for a bank). As you only manage that up to a certain point, you need to go with the market dynamics as a bank. At lower interest rates you will prefer loans with decent collateral. Residential houses for mortgages and physical capital for corporate loans both constitute collateral but not of the same quality. More supply of bank funding triggers more demand for both types of loans. But housing supply is more inelastic than business loan demand. The consequence is rising housing prices, thereby increasing the collateral value received by the banks (larger than the collateral of business loans). The reallocation by banks of funding from business loans to mortgage loans makes sense. Collateral with a low supply elasticity demonstrate more price and credit supply fluctuations than collateral with a high(er) supply elasticity.

Regarding the relation between shadow banking, financial stability and deposit insurance: L. Voellmy, (2018), Shadow Banking and Financial Stability under Limit Deposit Insurance, in Shadow Banking: Financial Intermediation beyond Banks (Ed. Esa Jokivuolle), SUERF Conference Proceedings 2018/1, Larcier, pp. 107–111.

S.T. Omarova, (2018), Central banks, Systemic Risk and Financial Sector Structural Reform, in R. Lastra and P. Conti-Browen (eds.), Research Handbook on Central Banking, Edward Elgar Publishing, London. Also: S.R. Das et al., (2018), Systemic Risk and the Great Depression, NBER Working Paper Nr. 25405, December. Their systemic risk measure captures both the credit risk of an individual bank as well as a bank’s position in the network. They also find that the pyramid structure of the commercial banking system (i.e. the network’s topology) created more inherent fragility. They conclude that network measures such as the discussed eigenvector centrality combined with balance sheet insights provide good prediction regarding survivorship of banks during a crisis.

Liquid interbank markets tend to always lead to excessive liquidity creation (and bank leverage): M. Mink, (2016). Aggregate Liquidity and Banking Sector Fragility, De Nederlandsche Bank Working Paper Nr. 534, Amsterdam; H. DeAngelo, and R.M. Stulz, (2015), Liquid-Claim Production, Risk Management, and Bank Capital Structure: Why High Leverage is Optimal for Banks, Journal of Financial Economics, Vol. 116, Issue 2, pp. 219–236.

Banks constrained by the leverage ratio prefer to first sell assets that are liquid and held in small amounts, while banks constrained by the risk-weighted capital ratio and the liquidity coverage ratio need to trade off assets’ liquidity with their regulatory weights. Banks’ optimal liquidation strategies translate into moderate fire-sale losses even for extremely large solvency shocks. By contrast, severe funding shocks can generate significant losses. Thus models focusing exclusively on solvency risk may significantly underestimate the extent of contagion via fire sales. Moreover, when studying combined funding and solvency shocks, we find complementarities between the two shocks’ effects that cannot be reproduced by focusing on either shock in isolation. In detail: J. Coen, (2019), Taking Regulation Seriously: Fire Sales under Solvency and Liquidity Constraints, Bank of England Working Paper Nr. 793, April 26.

E. Ebrahimy, (2019), Inefficient Fire-Sales in Decentralized Asset Markets, IMF Working Paper Nr. WP/19/92, May, pp. 1–4. Let it now be the case that shadow banking entities are largely active in markets and segments with such intrinsic characteristics.

E. Ebrahimy, (2019), Ibid. pp. 24–25.

Also: B. Chang, (2018), Adverse Selection and Liquidity Distortion. The Review of Economic Studies, Vol. 85, Issue 1, pp. 275–306; J. Dow, and J. Han, (2018). The Paradox of Financial Fire Sales: The Role of Arbitrage Capital in Determining Liquidity, Journal of Finance, Vol. 73, Issue 1, pp. 229–274.

However, the regulatory framework has always been quite unstable, see: E.J. Janger, (2002), Muddy rules for Securitization, Fordham Journal of Corporate and Financial Law, Vol. 7, Issue 2, pp. 300–320.

M. Segoviano, B. Jones, P. Lindner and J. Blankenheim, (2013), Securitization: Lessons Learned and the Road Ahead, IMF Working Paper, WP/13/255.

There is an international regulatory market for regulatory arbitrage with material competition in which a large part of the (shadow) banking market participates, however, with some notable exceptions; see: N. Boyson et al., (2016), Why Don’t All Bank Practice Regulatory Arbitrage? Evidence from Usage of Trust-Preferred Securities, The Review of Financial Studies, Volume 29, Issue 7, July 1, pp. 1821–1859.

Those standards have been awaited and were under development by the Bank for International Settlements. A consultative document was released in 2013, see: BIS, (2013), Revisions to the Securitization Framework, December. The final securitization framework has been released; see BIS, (2014), Revisions to the Securitization Framework, Basel III Document, Basel Committee on Banking Supervision, Basel. Major changes include the drivers behind risk exposures and the regulatory capital that banks should hold when engaging in various types of securitizations. Ever since the release of those finals standards in 2014 further refinements and updates have been implemented (see also the chapter on securitizations). See: BIS, (2015), Capital Treatment for ‘Simple, Transparent and Comparable’ Securitizations, November 10 (Consultative Document); BIS, (2016), Revision to the Securitization Framework, July 11; BIS, (2017), Capital Treatment for Simple, Transparent and Comparable Short-Term Securitizations, July 6 (Consultative Document); Some of these documents have then later on been rewired with(in) the existing policy, see: BIS, (2017), Basel III: Finalizing Post-Crisis Reforms, December 7.

Y. Aksoy and H.S. Basso, (2015), Securitization and Asset Prices, CESifo Working Paper Nr. 5213, February. They demonstrate further that the creation of synthetic securities (the pooling and tranching of credit assets) relaxes both the funding and the risk constraints financial entities face, allowing them to increase balance sheet holdings. This increase in asset demand depresses the compensation for undertaking risk in the economy, confirming our empirical results. Crucially, we show that declines in the compensation for risk taking in equity and bonds due to securitization may not be related to a decline in actual risk.

Bank competition in its turn can induce excessive risk taking (through the credit supply channel) due to risk shifting (e.g. by lowering lending standards in the mortgage market) leading to real economic outcomes. See A.X. Feng, (2018), Bank Competition, Risk Taking and their Consequences: Evidence from the U.S. Mortgage and Labor Markets, IMF Working Paper Nr. WP/18/157, June. Also: C. Palmer, (2015), Why Did so Many Subprime Borrowers Default During the Crisis: Loose Credit or Plummeting Prices?, Working Paper, mimeo; J. Dagher and N. Fu, (2017), What Fuels the Boom Drives the Bust: Regulation and the Mortgage Crisis, The Economic Journal, Vol. 127 (June), pp. 996–1024; G. Haoyu et al., (2019), Rise of Bank Competition: Evidence from Banking Regulation in China, NBER Working Paper Nr. 25795, May. The latter document that performance of private banks rises under deregulation. Such deregulation leads to higher screening standards, lower interest rates and lower delinquency rates for corporate loans from entrant banks. In contrast, the performance of state-owned enterprises (SOEs) does not improve following deregulation. Deregulation also amplifies bank credit from productive private firms to inefficient SOEs due mainly to SOEs’ soft budget constraints.

M. Marques-Ibanez, Y. Altunbas and M. van Leuvensteijn, (2014), Competition and Bank Risk. The Effect of Securitization and Bank Capital, ECB Working Paper Series Nr. 1678.

See Y. Altunbas et al., (2017), Macroprudential Policy and Bank Risk, BIS Working Paper Nr. 646, June.

M.C. Turk, (2018), Securitization Reform after the Crisis: Regulation by Rulemaking or Regulation by Settlement, Review of Banking and Financial Law, Vol. 37, pp. 870–874. He raises three questions: Why did the Dodd-Frank regulations prove so ineffectual? Can regulation by settlement be justified as an adequate substitute for shortcomings in the conventional rulemaking process? And, what lies ahead for the future of securitization regulation? While answering the second questions he concludes that ‘when the entire set of agency-bank settlements are considered as a whole, they can been seen as imposing a Pigouvian tax on the specific market externality associated with securitization, and therefore come surprisingly close to a first-best policy intervention’. The broader conclusion of his article is that the securitization field is a policy space which remains poorly understood and is still in flux. See also: P. de Gioia Carabellese, (2017), Securitization and Structured Finance: from Shadow Banking to Legal Harmonization? in Iris H. Chiu & Iain MacNeil (eds.), Research Handbook on Shadow Banking: Legal and Regulatory Aspects, Edward Elgar; Steven L. Schwarcz, (2018), Securitization: Ten Years After the Financial Crisis: An Overview, Review of Banking and Financial Law, Vol. 37, pp. 757–769.

See in detail: FSB, (2012), Securities Lending and Repos: Market Overview and Financial Stability Issues, Interim Report of the FSB Workstream on Securities Lending and Repos, pp. 1–5.

FSB, (2012), pp. 6–10.

FSB, (2012a), Strengthening Oversight and Regulation of Shadow Banking. A Policy Framework for Addressing Shadow Banking Risks in Securities Lending and Repos.

FSB, (2013), Strengthening Oversight and Regulation of Shadow Banking. A Policy Framework for Addressing Shadow Banking Risks in Securities Lending and Repos.

A. Shleifer and R. Vishny, (2011), Fire Sales in Finance and Macroeconomics, Journal of Economic Perspectives, Vol. 25 (Winter), pp. 30 ff.

See J.C. Stein, (2013), The Fire-Sales Problem and Securities Financing Transactions, Speech October 3, At the Federal Reserve Bank of New York Workshop on Fire Sales as a Driver of Systemic Risk in Tri-party Repo and other Secured Funding Markets, New York, NY.

G. Antinolfi, F. Carapella, C. Kahn, A. Martin, D. Mills and E. Nosal, (2013), Repos, Fire Sales and Bankruptcy Policy, Federal Reserve Bank of Chicago Working Paper Nr. 2012-15.

See for the historical context: A. Copeland, A. Martin, and M. Walker, (2010), The Tri-Party Repo Market before the 2010 Reforms, Federal Reserve Bank of NY Staff Report Nr. 477 and A. Copeland, D. Duffie, A. Martin, and J. McLaughlin, (2012), Key Mechanics of the U.S. Tri-Party Repo Market, Federal Reserve Bank of New York Economic Policy Review, Vol. 18, Issue 3, pp. 17–28.

B. Begalle, A. Martin, J. McAndrews, and S. McLaughlin, (2013), The Risk of Fire-Sales in the Tri-Party Repo Market, Federal Reserve Bank of New York Staff Reports, Nr. 616.

In more recent years central banks have also provided support to securities markets, see: D. King et al., (2017), Central Bank Emergency Support to Securities Markets, IMF Working Paper Nr. WP/17/152, July. King et al. tend to agree with the idea that, within the context of the central banks’ mandate with respect to financial stability, emergency support to securities markets is an important part of the crisis management response. But it needs to come with a serious amount of scrutiny. It should focus on ‘(1) ensuring that credit flow is not unduly disrupted resulting in a detrimental impact on the real economy; and (2) mitigating the risk that financial asset fire sales could threaten solvency in important parts of the economy’. (p. 38) And it should only come as part of a comprehensive policy package and as a stepwise process (p. 39). That package should take into account questions such as ‘what types of securities markets may be important for financial stability, what market conditions could trigger emergency support measures, and how programs can be designed to restore market functioning while minimizing moral hazard’.

See also: G. Gorton, and A. Metrick, (2012), Securitized Banking and the Run on Repo, Journal of Financial Economics, Vol. 104, Issue 3, pp. 425–451; A. Krishnamurthy, S. Nagel, and D. Orlov, (2012), Sizing Up Repo, NBER Working Paper, Nr. 17768; A. Martin, D. Skeie, and E.-L. von Thadden, (2010), Repo Runs, Federal Reserve Bank of New York Staff Report, Nr. 444; A. Martin, D. Skeie, and E.-L. von Thadden, (2012), The Fragility of Short-Term Funding Markets, Working Paper; C. Merrill, T. D. Nadauld, R. M. Stulz, and S. M. Sherlund, (2012), Why Did Financial Institutions Sell RMBS at Fire Sales Prices During the Financial Crisis? Manuscript, unpublished.

A fire sale in this scenario is caused by a flight from maturity (‘shortening maturities’); see in detail: G.B. Gorton et al., (2014), The Flight from Maturity, NBER Working Paper, Nr. 20027.

V.V. Acharya, and S. Öncü, (2013), A Proposal for the Resolution of Systemically Important Assets and Liabilities: The Case of the Repo Market, International Journal of Central Banking, Vol. 9, Issue 1, pp. 291–350.

T. Adrian, and H. S. Shin, (2010), Liquidity and Leverage, Journal of Financial Intermediation, Vol. 19, pp. 418–437.

A. Shleifer and R. Vishny, (1992), Liquidation Values and Debt Capacity: A Market Equilibrium Approach, Journal of Finance, Vol. 47, Issue 4, pp. 1343–1366.

R. Greenwood, A. Landier, and D. Thesmar, (2012), Vulnerable Banks, NBER Working Paper, Nr. 18537.

A first attempt is made. See: F. Duarte and T.M. Eisenbach, (2015), Fire-Sale Spillovers and Systemic Risk, Federal Reserve Bank of NY Working Paper, Nr. 645, revised edition (initial edition October 2013); A. Falato, (2016), Fire-Sale Spillovers in Debt Markets, Working Paper, August 24, Mimeo.

H.M. Ennis, (2011), Strategic Behavior in the Tri-Party Repo Market, Economic Quarterly, Vol. 97, Nr. 4, pp. 389–413.

FSB, (2014), Strengthening Oversight and Regulation of Shadow Banking, Regulatory Framework for Haircuts on Centrally-Cleared Securities Financing Transactions, October 14.

There are more specifically two notions of re-hypothecation. The first (narrow) notion of re-hypothecation relates to how broker-dealers (and no other market participants) should handle the securities of their customers: If they can use their customers’ securities as they see fit, we say that broker-dealers enjoy a re-hypothecation right. The second notion, as proposed by the International Swaps and Derivatives Association (ISDA), applies to any secured lender, not only to broker-dealers: The right of re-hypothecation refers to the right of a secured party to sell, pledge, re-hypothecate (in its narrow definition above), assign, invest, use, commingle or otherwise dispose of posted collateral. In what follows, I will use the broader definition of re-hypothecation, which, simply put, says that a lender with collateral can use it as if it was his or her own asset; see: C. Monnet, (2011), Rehypothecation, Philadelphia FED Business Review, quarter 4, pp. 18–25.

There are some countries in which there are limitations on how much (of their total collateral portfolio) an intermediary can put to work this way; however, in most countries it is uncapped.

M. Singh and J. Aitken, (2010), The (Sizable) Role of Rehypothecation in the Shadow Banking Sector, IMF Working Paper, WP/10/172.

Collateral velocity is anno 2019 back on the rise. See: M. Singh, (2019), Collateral Velocity is Rebounding, FT Alphaville, May 22.

Among the greatest benefits of re-hypothecation is capital efficiency in funding intermediation. Because collateral is repledged, less capital is needed to fund new debt or yield-seeking activities. Re-hypothecation reduces the cost of pledging collateral, leveraging a greater amount of funding on a relatively smaller capital base. However, there are significant risks when leverage and financial layering become too complex and opaque to discern whether a reasonable capital cushion exists to cover potential asset price declines (infra); see: D. Luttrel, H. Rosenblum and J. Thies, (2012), Understanding the Risks Inherent in Shadow Banking. A Primer and Practical Lessons Learned, Dallas FED Staff papers, Nr. 18, pp. 35–40. Kahn and Park highlight that ‘rehypothecation has trade-off effects; it enhances provision of funding liquidity to the economy so that additional productive investments can be undertaken, but incurs deadweight cost by misallocating the asset among the agents when it fails’. It is clear that the re-hypothecation model rarely delivers a socially optimal outcome. See in detail: C.M. Kahn and H.J. Park, (2019), Collateral, Rehypothecation and Efficiency, Journal of Financial Intermediation, Elsevier, vol. 39(C), pages 34–46. Also: J.R. Donaldson and E. Micheler, (2018), Resalable debt and Systemic Risk, Journal of Financial Economics, Vol. 127, pp. 485–504. Their point of focus is the heterogeneous nature of the resalability of debt claims. Higher market frictions lead to an increase in borrowing via non-resalable debt, but that comes with a problem, that is, it causes credit chains to form. A bank that needs liquidity cannot sell a non-resalable instrument and has to engage in a new contract. The credit chains are a source of systemic risk. A default of a bank harms its creditor and their creditors.

M. Brunnermeier, and L. Pedersen, (2008), Market Liquidity and Funding Liquidity, Review of Financial Studies, Vol. 22, Issue 6, pp. 2201–2238.

C. Johnson, (1997), Derivatives and Rehypothecation Failure. It’s3:00 pm. Do You Know Where Your Collateral Is? Arizona Law Review, Vol. 30, pp. 949 ff.

D. Andolfatto et al., (2015), Rehypothecation and Liquidity, Federal Reserve Bank of St. Louis, Working Paper Nr. 2015-003B, May.

CGFS, (2017), Repo Market Functioning, CGFS Working Paper Nr. 59, April.

Based on the security market program (SMP) it could be concluded that ‘specialness is affected by the amount of a security that is effectively available for trading on the market. This results from the combined effect of the auction cycle, the amount of securities that resides in the portfolio of buy-to-hold investors and security-specific demand, e.g. arising from shortellers. Specialiness drops after a new issuance takes place and securities are less available for trading tend to be more special. Bonds bought by the central bank through the SMP tended to have higher specialness and purchases likely affected also the distribution of specialness, inducing a larger right-hand tail’. See in detail: S. Corradin and A. Maddalino, (2017), The Importance Being Special: Repo Markets during the Crisis, ECB Working Paper Nr. 2065, May.

BIS, (2013), Asset Encumbrance, Financial Reform and the Demand for Collateral Assets, Report submitted by a Working Group established by the Committee on the Global Financial System, CGFS Papers, Nr. 49; J.C. Lopez, R. Mendes and H. Vickstedt, (2013), The Market for Collateral: The Potential Impact of Financial Regulation, Bank of Canada, Financial System Review (June), pp. 45–53. See also: T. Ahnert et al., (2016), Asset Encumbrance, Bank Funding and Financial Fragility, Deutsche Bundesbank Discussion Paper, Nr. 17, Frankfurt am Main (also as LSE Systemic Risk Centre Discussion Paper Nr. 83, September). Ahnert et al. focus on covered bonds. They have been used extensively particularly in Europe and are considered the cornerstone of bank funding. Theoretical analysis however is lacking and this against the background of increasing concerns regarding the financial stability implications of the collateralization of bank balance sheets. From a legal perspective, asset encumbrance is a claim against a property by another party. From a financial perspective, such claims have traditionally taken the form of security interests, such as pledges, given on assets by a borrower to a lender. Asset encumbrance affects bank fragility. Certain (discussed) features make covered bonds safe assets and a cheap source of funding. At the same time, they argue, these features asymmetrically shift risks onto unsecured creditors, which can heighten bank fragility and increase the cost of unsecured funding. They show that a bank’s usage of covered bonds balances this trade-off between profitability and fragility. The EBA releases an annual Report on asset encumbrance; latest edition: September 2019.

The other part of regulation focuses on more transactions being cleared over a central clearing party (CCP); see: D. Duffie, M. Scheicher and G. Vuillemey, (2014), Central Clearing and Collateral Demand, ECB Working Paper Series Nr. 1638.

M. Singh, (2010), Undercollateralisation and Rehypothecation in the OTC Derivative Markets, Financial Stability Review (July), pp. 113–119.

Short-term market rates are effectively determined in the pledged collateral market, where banks and other financial institutions exchange collateral (such as bonds and equities) for money. Singh and Goel document and develop a methodology to show that transactions using long dated collateral also affect short-term market rates. The implication is that the (future) unwinding of central bank balance sheets will likely strengthen the monetary policy transmission, as dealer balance-sheet space is now relatively less constrained, with a rebound in collateral reuse. See in detail: M. Singh and R. Goel, (2019), Pledged Collateral Market’s Role in Transmission to Short-Term Market Rates, IMF Working Paper Nr. WP/19/106, May.

V. Maurin, (2014), Re-Using the Collateral of Others: A General Equilibrium Model of Rehypothecation, European University Institute Working Paper.

See in detail: S.L. Schwarcz, (2010), Distorting Legal Principles, The Journal of Corporate Law, Vol. 35, Issue 4, pp. 697–727. However, its distortion of ‘nemo dat’ creates uncertainty (in the case of re-hypothecation) when customer securities become subject to claims of an intermediary‘s creditors, to whom the securities have been re-hypothecated. If customer securities were to become subject to those claims, customers could lose their securities if the intermediary fails. Although re-hypothecation is a long-standing practice (one may always grant a security interest in property to the extent of one’s rights therein), its operational execution has changed and become flawed. Lehman Brothers Inc. (e.g.), like many other prime-brokerage intermediaries, insisted that customers contractually consent to allow the intermediary to directly re-hypothecate the customers’ securities as collateral for financing obtained by the intermediary. The practice is conceptually flawed in that the intermediary does not own those securities but merely holds those securities on behalf of its customers, who at most give the intermediary a security interest in those securities. Lacking ownership of the customers’ securities, the intermediary should not be able, under the principle of ‘nemo dat’, to grant a security interest that enables its creditors to obtain ownership of those securities through foreclosure. Conceptually, therefore, Lehman and other prime-brokerage intermediaries ignored nemo dat when engaging in this form of re-hypothecation (pp. 702–703); For an evaluation of the net benefit or harm of the technique see pp. 705–711); see also M. Singh and J. Aitkin, (2009), Deleveraging after Lehman - Evidence from Reduced Rehypothecation, IMF Working Paper, WP/09/42.

See in detail: M. Singh, (2011), Velocity of Pledge Collateral: Analysis and Implications, IMF Working Paper, WP/11/256.

A. Copeland, D. Duffie, A. Martin and S. McLaughlin, (2012), Key Mechanics of the U.S. Tri-Party Repo market, FRBNY, Working Paper.

A most notable example is that of MF Global, which went bankrupt in 2011. MF Global’s demise is often attributed to its use of off-balance-sheet repurchase agreements called ‘repo-to-maturity’. The repo-to-maturity transactions involved borrowing billions of dollars backed by European sovereign debt due to expire at the same time. Because the loan collateral and the loan itself were set to mature simultaneously, MF Global was allowed to treat the transaction as a ‘sale’ under generally accepted accounting principles.

FSB, (2017), Transforming Shadow Banking into Resilient Market-based Finance. Re-hypothecation and Collateral Re-Use: Potential Financial Stability Issues, Market Evolution and Regulatory Approaches, January 25, London.

J. Authors, (2014), Democratization of Finance, Financial Times, June 23; P. Jenkins, (2014), Into the Shadows: Taking Another Path, Financial Times, June 16 and P. McCulley, (2014), Make Shadow Banks Safe and Private Money Sound, Financial Times, June 16.

The technology stock bubble of the late 90s had very few lasting economic effects on the economy due to this once it deflated.

The relationship between financial innovators and regulators has been historically tense, with financial innovators taking advantage of loopholes and regulators desperately trying to keep pace with innovations while dealing with limited resources and long bureaucratic processes. Nonetheless, in recent years, FinTechs—unregulated start-ups applying technology to finance—have exponentially accelerated such race, making new regulatory approaches and perspectives necessary. See in detail: E. Macchiavello, (2017), Financial-Return Crowdfunding and Regulatory Approaches in the Shadow Banking, Fintech and Collaborative Finance Era, European Company and Financial law Review, Vol. 14, Issue 4, pp. 662–722, December.

D. Luttrel, H. Rosenblum and J. Thies, (2012), Understanding the Risks Inherent in Shadow Banking: A Primer and Practical Lessons Learned, Dallas FED Staff papers, Nr. 18, p. 38.

Regarding the evaluation after the 1930 crisis see in detail: K.J. Mitchener and K. Wandschneider, (2014), Did Capital Controls Help Countries Recover from the Great Depression of the 1930s, NBER Working paper, Nr. 20,220, June 26. Similar results have been yielded regarding the aftermath of the 2008 crisis; V.N. Landi and A. Schiavone, (2018), The Effectiveness of Capital Controls, Bank of Italy Working Paper Nr. 1200, November 6. They conclude: controls reduce the volume of capital flows, even if the effects vary across investment types and between emerging and advanced economies. Controls tend to reduce the volatility of capital flows and foreign exchange borrowing. The effects on domestic credit are driven by advanced economies. Finally, capital controls are associated with a depreciation of the exchange rate in relation to the equilibrium level. Also: V.N. Landi, (2018), Capital Control Spillovers, Bank of Italy Working Paper Nr. 1184, July 20. The results show that the tightening of capital controls in an emerging economy induces the advanced economy to move a considerable share of foreign investments to the other emerging country, whose business cycle becomes more volatile. Moreover, the results show that coordination between the two emerging countries produces some benefits for both those economies, though only slightly greater compared with the scenario without coordination.

See for a current state of affairs: BIS, (2018), Globalization and Deglobalization, BIS Papers Nr. 100, December with a compilation of articles on the topic. Also: R.N. McCauley et al., (2017), Financial Deglobalization in Banking, BIS Working Paper Nr. 650, June 29, via bis.​org

They can come in many different natures; to start with the difference in gains from globalization. Lang and Tavares examined globalizations and observe differential gains from globalization both across and within countries: Income gains are substantial for countries at early and medium stages of the globalization process, but the marginal returns diminish as globalization rises, eventually becoming insignificant. Within countries, these gains are concentrated at the top of national income distributions, resulting in rising inequality. See in detail: V.F. Lang and M.M. Tavares, (2018), The Distribution of Gains from Globalization, IMF Working Paper Nr. WP/18/54, March. The gains from globalization are unequally both across and within countries. To be precise ‘global inequality of incomes between individuals is increasingly driven by within-country inequality and decreasingly by between-country inequality’ (p. 39). See also: D. Acemoglu, et al., (2019), Democracy Does Cause Growth, Journal of Political Economy, Vol. 127, Issue 1, pp. 47–100; F. Alvaredo, (2017), Global Inequality Dynamics: New Findings from WID. World, American Economic Review, Vol. 107, Issue 5, pp. 404–409; P. Antràs, et al., (2017), Globalization, Inequality and Welfare, Journal of International Economics, Vol. 108, pp. 387–412; T. Cordella, and A. Ospino, (2017), Financial Globalization and Market Volatility, World Bank Policy Research Working Paper Nr.8091; F. Dorn, et al., (2018), Globalization and Income Inequality Revisited, CESifo Working Paper Nr. 6859; D. Furceri, and P. Loungani, (2018), The Distributional Effects of Capital Account Liberalization, Journal of Development Economics, Vol. 130, pp. 127–144; D. Rodrik, (2017), Populism and the Economics of Globalization, NBER Working Paper Nr. 23559; M. El Hamiani Khatat and R. Veyrune, (2019), Liquidity Management under Fixed Exchange Rate with Open Capital Account, IMF Working Paper Nr. WP/19/58, February.

Freedom without restrictions has never yielded an optimal long-term optimum (short-term seems unsatisfying as well, as it cannot be sustained). Optimization requires balancing resources and capabilities over longer periods of time and carefully monitoring the net benefits of efforts undertaken so as to ensure the accumulation of wealth across generations.

Insofar one can identify credit booms and busts with unlimited capitalism, the understanding is emerging that unconstrained capitalism is actively damaging society and that the libertarian views on free markets do not correspond with the historical evidence of how free markets were designed and operated. See recently, for instance, N. Shaxson, (2018), The Finance Curse. How Global Finance is Making is Al Poorer, October, Bodley Head, London, UK; O. Bullough, (2018), Moneyland. Why Thieves & Crooks Now Rule the World & How to Take it Back, Profile Books Ltd., London, UK.

Regarding the linkages between asset prices and macroeconomic output there is a lot of literature (see e.g. S. Claessens and M.A. Kose, (2017), Asset Prices and Macroeconomic Outcomes: a Survey, BIS Working Paper Nr. 676, November for a literature overview), but the last word has not been said on many questions. Both at the level of the theoretical linkages as well as empirical evidence supporting these linkages many questions remain unanswered and many answered questions are still somewhat nebulous and this across all major asset classes.

O. Jeanne and A. Korinek, (2010), Managing Credit Booms and Busts: A Pigovian Tax Approach, NBER Working Paper Series, Nr. 16377. Also: O. Jeanne, (2012), Capital Flow Management, American Economic Review, Vol. 102, Issue 3, pp. 203–206.

They do so by using a specific starting point: ‘consider a group of individuals (the insiders) who enjoy a comparative advantage in holding an asset and who can use this asset as collateral on their borrowing from outsiders. The borrowing capacity of insiders is therefore increasing in the price of the asset. The price of the asset, in turn, is driven by the insiders’ consumption and borrowing capacity. This introduces a mutual feedback loop between asset prices and credit flows: small financial shocks to insiders can lead to large simultaneous booms or busts in asset prices and credit flows’ (p. 2). The same occurs on the level between countries: see M. Gelman et al., (2016), Transmission of Global Financial Shocks to EMU Member States: the Role of Monetary Policy and National Factors, Deutsche Bundesbank Discussion Paper Nr. 23, Frankfurt am Main. Financial shocks and the credit contraction it causes obviously also have an impact on the real economy and markets as ‘unemployment’. See in detail: F. Berton et al., (2017), Banks, Firms and Jobs, Working Paper Nr. 17/38, February.

Jeanne et al., Ibid. p. 2.

(1) They change the nature of the shock by assuming that it affects the availability of credit rather than the income of insiders. Then they look at the case where insiders can issue (2) long-term debt or (3) equity. All three of these extensions change some features of the boom-bust cycle equilibrium, but it remains true that the constrained optimum can be achieved by a countercyclical tax on debt, and this tax is of the same order of magnitude as in the benchmark model. Finally, they compare ex ante prudential taxation to ex post interventions that provide funds to constrained borrowers in a bust. They find that a bailout insurance fund that accumulates resources in good times and transfers them to debtors in a bust does not increase welfare (unless the resources are levied through the optimal Pigovian tax) (p. 3).

See among others G. Lorenzoni, (2008), Inefficient Credit Booms, Review of Economic Studies Vol. 75, Issue 3, pp. 809–833.

The case where the sensitivity of the credit constraint to the collateral price is large enough to produce multiple equilibria and self-fulfilling asset price busts (p. 33).

See in extenso: IMF, (2009), What’s the Damage? Medium-Term Output Dynamics after Financial Crises, Chapter 4, World Economic Outlook, pp. 121–151.

If there is nominal stickiness, a monetary restriction that raises the real interest rate in the boom should have the same macroprudential effect as the Pigovian tax discussed (Jeanne et al., Ibid. p. 34)

See, for instance, the determinants of the probability of withdrawals of capital inflows in (Asian) emerging economies: C.-s. Tam and Ip-wing Yu (2017), Determinants of the Probability of Abrupt Contraction in Gross Capital Inflows to Emerging market Economies, HKMA Research Memorandum 08/2017, June 15. Examples are: countries with higher levels of debt denominated in foreign currencies, a weaker equity market, weaker global growth, a decrease in foreign reserves, a narrowing growth differential between emerging countries and the West, … Also: O. Merrouche et al., (2017), Capital Inflows, Monetary Policy, and Financial Imbalances, Journal of International Monetary and Finance, Vol. 77, pp. 117–142.

See in detail: E. Nier and T. Sadi Sedik, (2014), Gross Private Capital Flows to Emerging Markets: Can the Global Financial Cycle be Tamed, IMF Working Paper Nr. WP/14/196. Cross-border bank flows are greater between source countries with more and target countries with less de iure bank regulation, which indirectly provides evidence of regulatory arbitrage in international bank flows. See in detail: G.A. Karolyi et al., (2017), Cross-Border Bank Flows and Systemic Risk, Working Paper, March 13, mimeo. Regarding the drivers of capital flows to emerging markets see in extenso: S.A. Hannan, (2017), The Drivers of Capital Flows in Emerging Markets Post Global Financial Crisis, IMF Working Paper Nr. WP/17/52, February, later on published in Journal of International Commerce, Economics and Policy, Vol. 8, Nr. 2, pp. 1–28. To be precise: ‘the capital flow slowdown witnessed in recent years is due to a combination of lower growth prospects of recipient countries and worse global risk sentiment. However, the determinants of flows can be considerably different across instruments and across the type of flows considered’. More erratic are drivers such as the gap between the US long- and short-term maturity bond yields, trade openness and interest rate differentials can play a role during high episodes but not so much during normal times.

See regarding the bank vs. non-bank credit cycles: C. Bora Durdu and M. Zhong, (2019), Understanding Bank and Nonbank Credit Cycles: A Structural Exploration, Finance and Economics Discussion Series Nr. 2019-031. Washington: Board of Governors of the Federal Reserve System, https://​doi.​org/​10.​17016/​FEDS.​2019.​031; E. Kemp et al., (2018), ‘The Non-Bank Credit Cycle, Finance and Economics Discussion Series’ Nr. 2018-076. Washington: Board of Governors of the Federal Reserve System, https://​doi.​org/​10.​17016/​FEDS.​2018.​076. The cyclical properties of non-bank credit cycles differ from those of bank credit. Non-bank credit cycles are highly correlated with bank credit cycles in some countries but not in others. Moreover, non-bank credit cycles are less synchronized than bank credit cycles across countries. Finally, non-bank credit cycles could act as a leading indicator for currency, but not for systemic banking crises. The opposite is true for bank credit cycles.

Y. Hashimoto and S. Krogstrup, (2019), Capital Flows: The Role of Bank and Nonbank Balance Sheets, IMF Working Paper Nr. WP/19/85, April, pp. 4–5, 24–25. See also: L.S. Goldberg, and S. Krogstrup, (2018), International Capital Flow Pressures, NBER Working Paper Nr. 24286, February; N.-J. Hansen, and S. Krogstrup, (2019), Capital Flows and Global Factors in Korea: An Investor Base Perspective, mimeo, International Monetary Fund.

Y. Hashimoto and S. Krogstrup, (2019), Ibid. p. 24. Also: S. Krogstrup, and C. Tille, (2018), Foreign Current Funding and Global Factors, Working Paper The Graduate Institute of Geneva, mimeo; N. Magud, (2018), Discussion of Capital Flows: The Role of Bank and Nonbank Balance Sheet, presentation at the Research Department workshop on Too Many Objectives or Too Few Instruments? Economic Policy Challenges Ten Years After the Crisis. March 22; S. Avdjiev, et al. (2017), The Shifting Drivers of Global Liquidity, CEPR Working Paper Nr. 12127.

In a way they still are, although it is acknowledged that policy design is still ‘incomplete’. See for guidance: G. Pasricha, (2017), Policy Rules for Capital Controls, Bank of Canada Staff Working Paper Nr. 42, October.

See for support: M. Saeed Qureshi et al., (2014), Regulating Capital Flows at Both Ends. Does it Work, IMF Working Paper Nr. WP/14/188. They focus on the coordination between source and recipient countries as a policy tool to manage cross-border capital flows and the spillover effects they might create. Cross-border capital flows may destabilize economic activity in particular if the domestic financial system is underdeveloped and institutions are weak: C. Buch, (2017), Macroprudential Measures and Capital Controls: Towards a Framework for Policy Evaluation, Banque de France Financial Stability Review, April, Nr. 21, pp. 157–166. Also: A.R. Ghosh, et al. (2016), When Do Capital Inflow Surges End in Tears? American Economic Review, Vol. 106, Issue 5, pp. 581–585; D.J. Scott and I. Presno, (2017), Capital Controls and Monetary Policy Autonomy in a Small Open Economy, International Finance Discussion Papers Nr. 1190, February. The former conclude that shocks to capital flows into a small open economy lead to volatility in asset prices and credit supply. Typically, that process is managed by the central bank using the domestic interest rate to manage the capital account. The use of capital controls allows to work more on domestic variables rather than the foreign interest rate.

S.L. Schwarcz, (2017), The Financial Crisis and Credit Unavailability: Cause or Effect, CIGI Policy Brief Nr. 98, February.

Experiences with capital controls in small economies are consistently positive. However, in large open economies where domestic financial constraints may bind following a large negative shock, Scott Davis et al. consider both ex ante capital controls (prudential) and ex post controls (crisis management). In a large open economy, there is a tension between the desire to tax capital inflows to manipulate the terms-of-trade and tax capital outflows for either prudential or crisis management purposes. When capital controls are chosen non-cooperatively, they show that ex post capital controls are unsuccessful in alleviating financial constraints in a crisis, and ex ante capital controls are unsuccessful at reducing financial instability before the crisis. Non-cooperative capital controls leave the crisis-hit country even worse off than in an environment with unrestricted capital flows. See for details: J. Scott Davis and M.B. Devereux, (2019), Capital Controls as Macro-Prudential Policy in a Large Open Economy, NBER Working Paper Nr. 25710, March.

O. Jeanne and A. Korinek, (2010), Excessive Volatility in Capital Flows: A Pigovian Taxation Approach, NBER Working Paper Series, Nr. 15927. Later on A. Korinek, (2018), Regulating Capital Flows to Emerging Markets: An Externality View, Journal of International Economics, Vol. 111, pp. 61–80.

Mainly to and from emerging markets in their case. See in more recent times about the correlation between capital inflows and outflows: J.S. Davis and E. van Wincoop, (2017), Globalization and the Increasing Correlation between Capital Inflows and Outflows, Federal Reserve Bank of Dallas, Globalization and Monetary Policy Institute, Working Paper Nr. 323, August. The correlation between capital inflows and outflows has increased substantially over time due to financial globalization, both in advanced as well as emerging economies. They conclude ‘[t]rade globalization has the exact opposite effect, but has been significantly dominated by financial globalization in the last several decades. Even if trade and financial globalization had grown proportionally, we find that the higher correlation between inflows and outflows due to financial globalization would still have dominated.’ (Ibid. p. 31). Also: E. Cerutti et al., (2017), How Important is the Global Financial Cycle? Evidence from Capital Flows, IMF Working Paper Nr. WP/17/193, September; F. Gourio, et al., (2016), Uncertainty and International Capital Flows, Stanford Working Paper, mimeo; F. Broner, and J. Ventura, (2016), Rethinking the Effects of Financial Globalization, The Quarterly Journal of Economics, Vol. 131, Issue 3, pp. 1497–1542; R. Correa et al., (2017), Cross-Border Bank Flows and Monetary Policy, Bank of Canada Working Paper Nr. 17-34, August; B. J. Eichengreen and P. Gupta, (2016), managing Sudden Stops, Policy Research Working Paper Nr. 7639.

Igan et al. draw some conclusion studying the real effect of capital flows: (1) the higher the volumes of capital inflows, the more external finance dependent industries emerge, (2) a reduction of output volatility occurs in case of equity inflows, (3) the nexus of capital flows and growth is more pronounced in countries with well-functioning banks. See in detail: D. Igan et al., (2016), Real Effects of Capital Inflows in Emerging Markets, IMF Working Paper Nr. 16/235, December. See for the effect of capital inflow in combination with financial deregulation within the context of the financial crisis: M. Hoffmann and I. Stewen, (2016), Holes in the Dike: the Global Savings Glut, U.S. House Prices and the Long Shadow of Banking Deregulation, CAMA Working Paper Nr. 6, February. Interesting in this paper is the fact that they break down in terms of the effect between states that opened up for out-of-state banks and those that did not. In those that did, house prices were more sensitive to capital inflows. Capital inflows relaxed the Value at Risk of integrated and regionally diversified US banks (more than local banks).

See also A. Korinek, (2010), Regulating Capital Flows to Emerging Markets; An Externality View, John Hopkins University Working Paper, updated as A. Korinek, (2017), Regulating Capital Flows to Emerging Markets: An Externality View, NBER Working Paper Nr. 24152, December.

M. Linn and D. Weagley, (2017), Systemic Risk and Firm Financial Constraints, Georgia Tech Scheller College of Business Research Paper Nr. 17-36.

Jeanne et al., (2010), p. 10.

With a 10% probability.

Capital controls have a bad reputation regardless of whether they are used as a tax instrument or not. Advanced economies use capital controls to tame speculative inflows. However several factors undermine their use as a prudential tool: (1) it appears that inflow controls became inextricably linked with outflow controls. The latter have typically been more pervasive, more stringent and more linked to autocratic regimes, failed macroeconomic policies and financial crisis—inflow controls are thus damned by this ‘guilt by association’; (2) capital account restrictions often tend to be associated with current account restrictions. As countries aspired to achieve greater trade integration, capital controls came to be viewed as incompatible with free trade; and (3) as policy activism of the 1970s gave way to the free market ideology of the 1980s and 1990s, the use of capital controls, even on inflows and for prudential purposes, fell into disrepute. See in detail for a great analysis on the matter: A.R. Ghosch et al., (2016), What’s In a Name? That Which We Call Capital Controls, MF Working Paper Nr. 16/25, February.

Chile had a similar measure in place during the period 1991–1998, See in extenso: F. Gallego, L. Hernandez and K. Schmidt-Hebbel, (2002), Capital Controls in Chile: Were They Effective?, Banking, Financial Integration, and Crises, L. Hernandez and K. Schmidt-Hebbel (ed.), Central Bank of Chile, Santiago, pp. 361 ff. See broader: G. Pasricha, et al., (2015), Domestic and Multilateral Effects of Capital Controls in Emerging Markets, ECB Working Paper Nr. 1844, August.

Interesting is that the decrease followed the signaling of the introduction of control rather than the introduction itself. See in detail: K. Forbes et al., (2012), Bubble Thy Neighbor: Portfolio Effects and Externalities from Capital Controls, Working Paper, Mimeo, April 27. Their focus is on the introduction of capital controls in Brazil.

See E. Takáts and J. Temesvary, (2019), How Does the Interaction of macroprudential and Monetary Policies Affect Cross-Border Bank Lending, BIS Working Paper Nr. 82, May 15, via bis.​org. Also: BCBS, (2019/2018), Survey on the Interaction of Regulatory Instruments: Results and Analysis, Working Paper Nr. 35 (March 2019), Working Paper Nr. 33, (July 2018). BCBS, (2016), Literature Review on Integration of Regulatory Capital and Liquidity Instruments, BCBS Working Paper Nr. 30, March.

See J. Beirne and C. Friedrich, (2015), Capital Flows and Macro-Prudential Policies- A Multilateral Assessment of Effectiveness and Externalities, Bank of Canada/Banque de Canada Working Paper Nr. 2014/31. Cerutti et al. assess five types of prudential instruments on the nature and type of effect: capital buffers (subcategories: general capital requirements, real state credit-specific capital buffers, consumer credit-specific capital buffers and other specific capital buffers), interbank exposure limits, concentration limits, loan to value (LTV) ratio limits and reserve requirements (subcategories: domestic currency capital requirements and foreign currency capital requirements). See: E. Cerutti et al., (2016), Changes in Prudential Policy Instruments–A New Cross-Country Database, IMF Working Paper Nr. 16/110, June.

The optimal policy mix in such models involves a combination of both types (i.e. ex ante and ex post) of measures since they offer alternative ways of mitigating binding financial constraints. Comparing their relative merits, ex post policy interventions are only taken once a crisis has materialized and are therefore better targeted, whereas ex ante measures are blunter since they depend on crisis expectations. However, ex post interventions distort incentives and create moral hazard. This introduces a time consistency problem, which can in turn be solved by ex ante policy measures. See for the very examined area of whether autonomous changes in expectations (i.e. changes in expectations that are not due to changes in fundamentals) cause cycle fluctuations: Z. Enders et al., (2017), Growth Expectations, Undue Optimism and Short-Run Fluctuations, Deutsche Bundesbank Discussion Papers Nr. 11/2017. Business sentiment seems to not only correspond to changes in current or future fundamentals, but is in itself a ‘genuine, psychological cause of (inefficient) business cycles aka undue optimism.

O. Jeanne and A. Korinek, (2013), Macro-prudential Regulation versus Mopping Up after the Crash, NBER Working Paper Series, Nr. 18675.

Financial regulation has adopted processes that are inconsistent with adherence to the rule of law. Calomiris refers to ‘flawed regulatory processes especially those related to the use of “guidance,” which avoids transparency, accountability and predictability, and thereby increases regulatory risk – has resulted in poor execution of regulatory responsibilities, unnecessary regulatory costs and opportunities for politicized mischief.’ See: C.W. Calomiris, (2017), Restoring the Rule of Law in Financial Regulation, CATO Working Paper Nr. 48, September. His baseline: regulatory practice should be grounded in formal rule making rather than the reliance on guidance.

Traditionally the focus has been on inefficiently high bank risk-taking in the run-up to financial crises. In contrast, Schroth focuses on inefficiently low bank risk-taking during financial crises. The reason for the latter inefficiency is that a regulator can mitigate the risk of binding bank funding conditions by increasing a bank’s future profits to offset decreases in bank equity. The first main policy implication is that banks should build up capital buffers during normal times. The second main policy implication is that banks should be given ample time to rebuild capital buffers following a financial crisis and that regulation should increase bank profitability in that process. The idea is to raise the prospect of future profitability during the financial crisis with a view to increasing a bank’s access to outside funding and reducing ex post the severity of a financial crisis. Schroth studies optimal bank capital requirements in a model of endogenous bank funding conditions and finds that requirements should be higher during good times such that a macroprudential ‘buffer’ is provided. However, whether banks can use buffers to maintain lending during a financial crisis depends on the capital requirement during the subsequent recovery. The reason is that a high requirement during the recovery lowers bank shareholder value during the crisis and thus creates funding-market pressure to use buffers for deleveraging rather than for maintaining lending. Therefore, buffers are useful if banks are not required to rebuild them quickly. See in detail: J. Schroth, (2019), Macroprudential Policy with capital Buffers, BIS Working Paper Nr. 771, February 22, via bis.​org

See A. Korinek and J. Kreamer, (2013), The Redistributive Effects of Financial Deregulation, NBER Working Paper Series, Nr. 19572.

You could call it international capital market pressures that occur. To visualize and understand a new measure of capital flow pressures in the form of a recast exchange market pressure index has been developed. The measure captures pressures that materialize in actual international capital flows as well as pressures that result in exchange rate adjustments. The input for the analysis is the balance of payments equilibrium conditions and international asset portfolio considerations. It therefore combines price and quantity information. Also global risk response index, which reflects the country-specific sensitivity of capital flow pressures to measures of global risk aversion. See in detail: L. Goldberg and S. Krogstrup, (2018), International Capital Flow Pressures, Federal Reserve Bank of New York Staff Reports, Nr. 834, February. They comment ‘[c]apital flows respond differently to global risk factors depending on whether a country’s monetary authorities intervene in foreign exchange markets to influence the local currency exchange rate, or whether capital flow pressures result in changes in the exchange rate or interest rate sufficient to discourage capital flow pressures from being realized in actual flows. In fully floating exchange rate regimes, capital flow pressures would materialize in exchange rate adjustments while in fixed exchange rate regimes, the price adjustment is prevented, and a capital flow is fully realized in response to the same pressure’ (p. 1). A closer look at the interesting literature list might be justified for those interested in the supporting literature that led to the design of the model.

See for if and how monetary easing increases financial instability: A. Cesa-Bianchi and A. Rebucci, (2015), Does Easing Monetary Policy Increase Financial Instability, IMF Working Paper, Nr. WP/15/139. An implication of their analysis is that the weak link in the US policy framework in the run-up to the Global Recession was not the excessively lax monetary policy after 2002, but rather the absence of an effective regulatory framework aimed at preserving financial stability. That is somewhat contradicting with the traditional view that a different interest rate policy would have reduced both the likelihood and severity of the Great Recession (p. 36). When a pallet of different instruments is available (besides interest rate policy), expansive monetary policy doesn’t have to increase financial instability. Also: A. Zdzienicka et al., (2015), Effects of Macroprudential Policies on Financial Conditions: Evidence from the U.S., Working paper Nr. 15/288, December. Monetary policy shocks have significant and persistent effects on financial conditions and can attenuate long-term financial instability. Cecchetti et al. observed that the leverage ratio, as well as other measures of firm-level vulnerability, increases for banks and non-banks as domestic monetary policy easing persists and therefore increases financial vulnerability. See in detail: S. Cecchetti et al., (2017), Does Prolonged Monetary Policy Easing Increase Financial Vulnerability, IMF Working Paper Nr. WP/17/65, February.

G. Plantin and H.S. Shin, (2016), Exchange Rates and Monetary Spillovers, BIS Working Paper Nr. 537, January; J. Kearns et al., (2019), Explaining Monetary Spillovers: The Matrix Reloaded, BIS Working Paper Nr. 757, November 20. Also here regulatory arbitrage plays a role, see: J. Temesvary, (2018), The Role of Regulatory Arbitrage in U.S. Banks’ International Flows: Bank-Level Evidence, Economic Inquiry, Vol. 56, Issue 4, pp. 2077–2098.

See regarding the most important sustainability dimension in macroprudential regulation: K.N. Johnson, (2013), Macroprudential Regulation: A Sustainable Approach To Regulating Financial Markets, University of Illinois Law Review, pp. 882–918; L.A. Górnicka, (2015), Regulating Financial Markets: Costs and Trade-Offs, Tinbergen Institute Research Series Nr. 621, Amsterdam; M. Rubio, (2018), Shadow Banking, Macroprudential Regulation and Financial Stability, Nottingham University Working Paper, September, mimeo.

See in detail: A. Korinek and D. Sandri, (2015), Capital Controls or Macroprudential Regulation?, IMF Working Paper Nr. WP/15/218. Also: G. Benigno et al., (2016), Optimal Capital Controls and Real Exchange Rate Policies: A Pecuniary Externality Perspective, Journal of Monetary Economics, Vol. 84, 147–165; in the context of a wider imperfect financial union (i.e. Europe): F. Balestrieri and S. Basu, (2018), An Imperfect Financial Union With Heterogeneous Regions IMF Working Paper Nr. WP/18/205, September.

A. Korinek and A. Simsek, (2016), Liquidity Trap and Excessive Leverage, American Economic Review, Vol. 106, Issue 3, pp. 699–738. Also: J. Lukkezen et al., (2016), Macro-Economics of Balance-Sheet Problems and the Liquidity Trap, CPB Background Document, August; T. Akram, (2016), Japan’s Liquidity Trap, Levy Economics Institute Working Paper Nr. 862, March. The latter concludes that generating effective demand might be a more appropriate remedy for the country’s liquidity trap.

I. Alonso Ávarez, (2015), Institutional Drivers of Capital Flows, Bank of Spain, Working Paper Nr. 1531.

As we have experienced in most parts of the Western world in recent years. A number of countries have stepped out of that rate race (Australia, Brazil, Russia, …) for a variety of reasons. See also in detail the proposition of international coordination in the context of a Pareto optimum through international coordination of policies: O. Jeanne, (2014), Macro-prudential Policies in a Global Perspective, NBER Working Paper Series, Nr. 19967.

S. Laseen et al., (2015), Systemic Risk: A New Trade-Off for Monetary Policy, IMF Working Paper, Nr. WP/15/142. Their study captures the non-linear behavior of financial variables and their interaction with the real economy. ‘These frictions (financial intermediaries try to maximize the return on intermediaries’ equity and fundraising is difficult during periods of financial distress) and their interaction with the real economy generate systemic risk. That can then be described as ‘a fall in asset prices that induces a sufficiently large decline in the return on financial intermediaries’ equity renders them unable to raise equity. As a result, their portfolio becomes riskier, prompting risk averse managers to require higher risk-adjusted returns in the future (the Sharpe ratio increases). To deliver the higher expected returns the price of capital must decline. Lower asset prices propagate the financial stress to the real economy by reducing the volume of investment in physical capital which results in a further deterioration in the macroeconomic environment, raising the possibility of a vicious cycle’ (p. 3).

See for the role of macroprudential policy in safeguarding financial stability: R. Vergara, (2015), The Role of Macroprudential Policy and Monetary Policy in Safeguarding Financial Stability, Speech given at the First Conference “Banking Development, Stability and Sustainability”, organized by the Chilean Superintendency of Banks and Financial Institutions and the Diego Portales University, Santiago, November 5. Cizel warns for the substitution effects (of macroprudential policy) toward non-bank credit, especially in advanced economies, reducing the policies’ effect on total credit. Quantity restrictions are particularly potent in constraining bank credit but also cause the strongest substitution effects. See: J. Cizel et al., (2016), Effective Macroprudential Policy: Cross-Sector Substitution from Prize and Quantity Measures, IMF Working Paper Nr. 16/94. Also: A. Prasad et al., (2016), Macroprudential Policy and financial Stability in the Arab Region, IMF Working paper Nr. 16/98, May. See also the annual review of macroprudential policy in the EU: ESRB, A Review of Macroprudential Policy in the EU, from 2016 onwards.

See for an overview of the effectiveness of macroprudential policies: E. Cerutti et al., (2017), The Use and Effectiveness of Macroprudential Policies: New Evidence, Journal of Financial Stability, Elsevier, Vol. 28(C), pp. 203–224.

For the operational dimensions see: ESRB, (2014), The ESRB Handbook on Operationalising Macro-Prudential Policy in the Banking Sector, March, Frankfurt am Main. The framework is built around four objectives: addressing (1) excessive credit growth and leverage, (2) excessive maturity mismatch and market illiquidity, (3) direct and indirect exposure concentration, and (4) misaligned incentives and moral hazard. See, for instance, the impact of macroprudential policies focused on leveraged lending: S. Kim et al., (2017), Macroprudential Policy and the Revolving Door of Risk: Lessons from Leveraged Lending Guidance, Federal Reserve Bank of New York Staff Reports, Nr. 815, May. They conclude that ‘[w]hile we do not find that nonbanks had more lax lending policies than banks, we unveil important evidence that nonbanks increased bank borrowing following the issuance of guidance, possibly to finance their growing leveraged lending. The guidance was effective at reducing banks’ leveraged lending activity, but it is less clear whether it accomplished its broader goal of reducing the risk that these loans pose for the stability of the financial system’.

Changes in macroprudential policy via loan-to-value limits and local currency reserve requirements have a significant impact on international bank lending. Balance sheet characteristics tend to play an important role in determining the strength of these effects, with better capitalized banking systems and those with more liquid assets and less core deposits reacting more. The tightening of macroprudential measures can be associated with international spillovers. See in detail: S. Avdjiev et al., (2016), International Prudential Policy Spillovers: A Global Perspective, BIS Working Paper Nr. 589, October.

O. Jeanne and A. Korinek, (2014), Macro-Prudential Policy Beyond Banking Regulation, Banque de France, Financial Stability Review, Nr. 18, April 2014, pp. 163–171.

S. Claessens, (2014), An Overview of Macro-Prudential Policy Tools, IMF Working Paper Series, Nr. WP/14/214.

Financial frictions are central to understanding the non-linearities observed during financial crises. Financial frictions lead to an amplification of cross-border shocks, but most empirical studies on macro-financial linkages resort to linear models that fail to account for the non-linear amplification mechanisms implied by the theoretical literature; See: N. Metiu et al. et al., (2015), Financial Frictions and Global Spillovers, Deutsche Bundesbank Discussion Paper, Nr. 04/2015. Also: M; Pietrunti, (2017), Financial Frictions and the Real Economy, ESRB Working Paper Nr. 41, April; R. Duval et al., (2017), Financial Frictions and the Great Productivity Slowdown, IMF Working Paper Nr. WP/17/129, May. Duval et al. demonstrate that the productivity slowdown post-crisis can be linked to the combination of pre-existing firm-level financial fragilities and tightening credit conditions. Or to be more precise: ‘(i) firms that entered the crisis with weaker balance sheets experienced decline in total factor productivity growth relative to their less vulnerable counterparts after the crisis; (ii) this decline was larger for firms located in countries where credit conditions tightened more; (iii) financially fragile firms cut back on intangible capital investment compared to more resilient firms, which is one plausible way through which financial frictions undermined productivity’.

Claessens, Ibid. p. 3

H. Huizinga and L. Laeven, (2019), The Procyclicality of Banking, ECB Working Paper Nr. 2288, June 7. Loan loss provisions in the euro area are negatively related to GDP growth, that is, they are procyclical they conclude. Loan loss provisions tend to be more procyclical at larger and better capitalized banks.

M. Houdala, (2018), Off the Radar: Exploring the Rise of Shadow Banking in the EU, Czech National Working Paper Nr. 16. The EU shadow banking system is found to be highly procyclical and positively related to increasing demand of long-term institutional investors, more stringent capital regulation and faster financial development. He further documents that the relationship between monetary policy and shadow banking growth is level-dependent and may be determined by the relative magnitude of interest rates in the economy. In this respect, two main motives driving the relationship are identified: the ‘funding cost’ motive and the ‘search-for-yield’ motive (pp. 15 ff.). Second, the driving forces of shadow banking differ between the old and new EU countries, largely due to the missing legal framework for securitization (which lowers the funding costs) in the new members. The funding cost motive dominates when interest rates are high, while the search for yield motive matters when interest rates are low (p. 16).

Regarding monetary policy for financial stability see: J. Caruana, (2016), Monetary Policy for Financial Stability, Speech at the 52nd SEACEN Governors’ Conference Naypyidaw, Myanmar, November 26, bis.​org

Claessens, Ibid. p. 5. He uses a more reduced number/categories of externalities than was done in our chapter. He limits himself to (1) externalities related to strategic complementarities, that arise from the strategic interactions of banks and other financial institutions and agents, and which cause the build-up of vulnerabilities during the expansionary phase of a financial cycle; (2) externalities related to fire sales and credit crunches, that arise from a generalized sell-off of assets causing a decline in asset prices, a deterioration of balance sheets of intermediaries and investors, and a drying up of financing, especially during the contractionary phase of a financial (and business) cycle; and (3) externalities related to interconnectedness, caused by the propagation of shocks from systemic institutions or through financial markets or networks (‘contagion’). The anticipation of bailouts perversely affects the (risk-taking) incentives for SIFIs and other market participants. It introduces a race among institutions to become systemically important, as this lowers the cost of funding, and reduces market discipline for creditors of SIFIs, especially the riskiest ones. See in detail: Claessens, Ibid. pp. 6–8.

J. Dagher, (2018), Regulatory Cycles: Revisiting the Political Economy of Financial Crises, IMF Working Paper Nr. WP/18/08, January. Dagher reviews ten previous financial crises and analyzes them from the perspective of a political economy and the regulatory implications. As Dagher indicates ‘[i]t shows that episodes of financial boom went hand in hand with a period of significant deregulation. These episodes were generally accompanied, and sometimes triggered, by procyclical policies by governments that actively amplified credit booms, weakened existing financial regulations and supervision, and engaged in regulatory forbearance. While in most cases the incumbent governments championed laissez-faire policies, they nevertheless engaged in subsidization of credit and intensification of symbiotic relations with bankers and large companies’ (p.3). After a crisis, policies were often reversed. Dagher is the first to closely examine the pattern of financial regulatory policies, their political economy and political consequences across different episodes, over time and across countries. Patterns of financial regulation are documented. It shows that in most cases ‘regulation has been pro-cyclical, effectively weakening during the boom and strengthening during the bust. Regulators do not operate in a vacuum, and [the] paper shows how, in most cases, political interventions have helped fuel the boom in similar ways across time and countries. The political repercussions of crises, partly due to changes in the public’s perception about the role of the government, are usually very significant’ (p. 61). Relevant in a time and age where the Volcker rule and the Dodd-Franck Act are prone to renewed deregulation efforts: B. Jopson and R. Wigglesworth, (2018), US Regulators Begin to Ease Volcker Rule, Financial Times, May 30.

E. Cerutti and H. Zhou, (2018), Cross-Border Banking and the Circumvention of Macroprudential and Capital Control Measures, IMF Working Paper Nr. WP/18/217, September.

Others have studied the impact of country-specific uncertainty as a key (pull and push) factor of international capital flows. See S. Choi and D. Furceri, (2018), Uncertainty and Cross-Border Banking Flows, IMF Working Paper Nr. WP/18/4, January. The findings, and this despite the challenges of observing uncertainty as it is often difficult to separate between credit demand and credit supply factors, can be summarized as being that ‘(i) uncertainty is both a push and pull factor that robustly predicts a decrease in both outflows (retrenchment) and inflows (stops); (ii) global banks rebalance their lending towards safer foreign borrowers from local borrowers when facing higher uncertainty; (iii) this rebalancing occurs only towards advanced economies (flight to quality), but not emerging market economies’. See also: E. Cerutti, et al., (2017), Global Liquidity and Cross-Border Bank Flows, Economic Policy Vol. 32.89, pp. 81–125; S. Choi, (2018), The Impact of US Financial Uncertainty Shocks on Emerging Market Economies: An International Credit Channel, Open Economies Review, Springer, Vol. 29, Issue 1, pp. 89–118, February; S. Choi, et al., (2018), Aggregate Uncertainty and Sectoral Productivity Growth: The Role of Credit Constraints, Journal of International Money and Finance, Vol. 88, pp. 314–330.

E. Cerutti and H. Zhou, (2018), Ibid. p. 4.

E. Cerutti and H. Zhou, (2018), Ibid. pp. 8–12.

E. Cerutti and H. Zhou, (2018), Ibid. pp. 18–19. See also on this matter: P.-R. Agenor and L.P. da Silva, (2018), Financial Spillovers, Spillbacks, and the Scope for International Macroprudential Policy Coordination, BIS Working Papers Nr. 97; A. Avdjiev et al., (2017), International Prudential Policy Spillovers: A Global Perspective, International Journal of Central Banking, Vol. 13, pp. 5–32; V. Bruno et al., (2017), Comparative Assessment of Macroprudential Policies, Journal of Financial Stability, Vol. 28, pp. 183–202; E. Cerutti et al., (2017), The Use and Effectiveness of Macroprudential Policies: New Evidence, Journal of Financial Stability, Vol. 28, pp. 203–224; K. Forbes et al., (2017), The Spillovers, Interactions, and (Un)intended Consequences of Monetary and Regulatory Policies, Journal of Monetary Economics, Vol. 85, pp. 1–22; J. Temesvary et al., (2018), A Global Lending Channel Unplugged? Does U.S. Monetary Policy Affect Cross-Border and Affiliate Lending by Global U.S. Banks? Finance and Economics Discussion Series Nr. 2018-008, Washington: Board of Governors of the Federal Reserve System.

See also regarding that analysis: S. Claessens et al., (2013), Macro-Prudential Policies to Mitigate Financial System Vulnerabilities, Journal of International Money and Finance, Vol. 39, pp. 153–185; M. Giannetti and F. Saidi, (2017), Shock Propagation and Banking Structure, Sveriges Riskbank Working Paper Series Nr. 348, December.

L. Nijs, (2015), Neoliberalism 2.0. Regulating and Financing Globalizing Markets, Palgrave, Basingstoke, chapters 2 and 3. See also more recently: T. Biebricher, (2019), The Political Theory of Neoliberalism, Stanford University Press, Palo Alto, CA; C. Barthold, (2018), Resisting Financialization with Deleuze and Guattari, Routledge, Abingdon; D. Cahill and M. Cooper, (2018), The SAGE Handbook of Neoliberalism, SAGE Publications, Thousand Oaks, CA; M. Eagleton-Pierce, (2016), Neoliberalism, Routledge, Abingdon; W. Davies, (2016), The Limits of Neoliberalism, Sage Publications, Thousand Oaks, CA; H. Cleaver, (2017), Rupturing the Dialectic: The Struggle Against Work, Money, and Financialization, AK Press, London.

Regulation is instrumental in that transition and codifying this economic ideology into law is central to the existence of the neoliberal discourse; see also: B. Farrand and Marco Rizzi, (2018), There Is No (Legal) Alternative: Codifying Economic Ideology into Law, in E. Nanopoulos & F. Vergis (eds.), The Euro-Crisis as a Multi-Dimensional Systemic Failure of the EU, Cambridge University Press, Cambridge; N. Perrone, (2017), Neoliberalism and Economic Sovereignty: Property, Contracts, and Foreign Investment Relations, in H. Brabazon (Ed.), Neoliberal Legality: Understanding the Role of Law in the Neoliberal Project, Routledge, Abingdon; V. Tarko, (2017), Neoliberalism and Regulatory Capitalism: Understanding The ‘Freer Markets More Rules’ Puzzle, Dickinson College Working Paper, mimeo.

See: T.I. Palley, (2008), Financialization: Why It Is and Why It Matters?, Levy Economics Institute Working Paper Nr. 525. Palley documents that its principal impacts are to (1) elevate the significance of the financial sector relative to the real sector, (2) transfer income from the real sector to the financial sector, and (3) increase income inequality and contribute to wage stagnation. Additionally, there are reasons to believe that financialization may put the economy at risk of debt deflation and prolonged recession. Brei et al. however document that the relationship between financial development and income inequality is not linear. Up to a point, more finance reduces income inequality. Beyond that point, inequality rises if finance is expanded through market-based financing, but not when finance grows via bank lending. See in detail: M. Brei et al., (2018), Financial Structure and Income Inequality, BIS Working Paper, Nr. 756, November 15, via bis.​org

A lot of the differences in definition can be traced back to issues and contestation that relate to the demarcation lines and in particular between financialization and commoditization on the one hand and marketability on the other. How does it relate to globalization and neoliberalization? See in detail E. Engelen, M. Konings and R. Fernandez, (2010), Geographies of Financialization in Disarray: The Dutch Case in Comparative Perspective, Economic Geography, 86(1), pp. 53–73.

G.A. Epstein (ed.), (2005), Financialization and the World Economy, Edward Elgar, Cheltenham, p. 3.

See for a literature review in this respect: I. Ertürk, J. Froud, S. Johal, A. Leaver and K. Williams (eds.), (2008), Financialization at Work, Routledge, Abingdon.

Or in fact subject. Under neoliberalism humans can be ranked on their ability to produce and consume and are appreciated in social terms based on the accumulated wealth they have so far produced (Forbes ranking) in their life. Even if it is inherited wealth, the commoditization process does not recognize the difference as the process is disconnected from every possible judgmental ability. In fact, the accumulated wealth indicator is often used as a proxy for (unproven) qualities in other fields (entrepreneurs becoming politicians. Whose opinion matters more: those who represent capital or who possess the better quality argument? It facilitates categorization and the ability to ignore subjects that do not preach free market dynamics, disqualifying them as populists or as marginal in nature (‘deplorables’).

In the case of shadow banking the argumentation can be as follows: the accumulation of leverage in the shadow banking system and the creation of credit money by the traditional banking sector are symbiotic processes. As such those processes operate a perverse form in which household debt is stored on the balance sheets of shadow banks, allowing the banking system to break the historical connection between money creation and productive activity. See in detail: J. Michell, (2016), Do Shadow banks Create Money? ‘Financialisation’ and the Money Circuit, PKSG Working Paper Nr. 1605 March. Aligned with Michell see: A. Botta et al., (2016), The Macro-economics of Shadow Banking, PKSG Working Paper Nr. 1611, June.

In the US. See also: R. Greenwood and. Scharfstein, (2012), The Growth of Modern Finance, HBS Working Paper.

Source: Haver analytics, Central Banking datasets (2013 numbers).

See: C.M. Reinhart and K. Rogoff, (2011), This Time is Different: Eight Centuries of Financial Folly, Princeton University Press, Princeton; H. Schwartze and L. Seabrooke, (2008), Varieties of Residential Capitalism in the International Political Theory: Old Welfare States and the New Politics of Housing, Comparative European Politics, Vol. 6, pp. 237–261.

See: D. MacKenzie, (2006), An Engine, Not a Camera: How Financial Models Shape Markets, MIT Press, Cambridge.

E. Engelen, R. Fernandez and R. Hendrikse, (2014), How Finance Penetrates its Other: A Cautionary Tale on the Financialization of a Dutch University, Antipode, March, pp. 1–20. That is quite remarkable given the long-standing tradition of self-managed guild-oriented public funded universities in continental Europe.

J. Tronto, (2017), There is an Alternative: Homines Curans and the Limits of Neoliberalism, International Journal of Care and Caring, Vol. 1, Issue 1, pp. 27–43.

I-H. Cheng and W. Xiong, (2013), The Financialization of Commodity Markets, NBER Working Paper Series, Nr. 19642.

L. Nijs, (2014), Global Agricultural Markets: The Handbook of Land, Water and Soft Commodities, chapter 16 on Speculation in soft commodity markets, Palgrave Macmillan, London, including a material literature review of the matter.

S. R. Isakson, (2013), The Financialization of Food: A Political Economy of the Transformation of Agro-Food Supply Chains, ICAS Review Paper Series Nr. 5; J. Clapp, (2012), The Financialization of Food: Who is Being Fed?, Waterloo University Working Paper, and also J. Clapp, (2013), Financialization, Distance and Global Food Politics, Conference Paper Nr. 5, Food Sovereignty: A Critical Dialogue, September 14–15.

M.C. Taylor, (2011), Financialization of Art, Capitalism and Society, Vol. 6, Issue 2, Article 3, updated in 2013. As well: N. Horowith, (2014), Art of the Deal: Contemporary Art in a Global Financial market, Princeton University Press, Princeton NJ, in particular pp. 143–187.

M. Hudson, (2010), The Transition of Industrial Capitalism to a Financialized Bubble Economy, Levy Economics Institute, Working Paper, Nr. 627; G.A. Epstein, (2005), Financialization of the World Economy, Edward Elgar, Cheltenham.

A. Sheng, (2013), The End of Financialization, Institute for New Economic Thinking, blog article.

S.G. Cecchetti and E. Kharroubi, (2012), Reassessing the Impact of Finance on Growth, BIS Working Papers Series, Nr. 381; C. Lapavitsas, (2014), Profiting Without Producing: How Finance Exploits Us All, Verso, NY and K. Polanyi-Hewitt, (2013), From the Great Transformation to the Great Financialization: On Karl Polanyi and Other Essays, Zed Books, London.

Ő.Orhangazi, (2008), Financialisation and Capital Accumulation in the Non-Financial Corporate Sector: A Theoretical and Empirical Investigation on the US Economy: 1973–2003, Cambridge Journal of Economics, Vol. 32, pp. 863–886.

They developed monthly measures of macroeconomic uncertainty covering 45 countries and constructed a measure of global uncertainty as the weighted average of country-specific uncertainties. Measure captures perceived uncertainty of market participants and derives from two components that are shown to exhibit strikingly different behavior; see in extenso: E. O. Ozturk and X.S. Sheng, (2017), Measuring Global and Country-specific Uncertainty, IMF Working Paper Series, nr. WP/17/219, October. Also: R. Rich and J. Tracy, (2017), The Behavior of Uncertainty and Disagreement and Their Roles in Economic Prediction: A Panel Analysis, Federal Reserve Bank of New York Staff Reports, Nr. 808, February. Rich and Tracy point at the difference between uncertainty and disagreement. Both are subjective magnitudes and therefore difficult to manage. Also: A. Haberis et al., (2017), Uncertain Forward Guidance, Bank of England Working Paper Nr. 654, March. S. Morris and H.S. Shin, (2018), Central Bank Forward Guidance and the Signal Value of Market Prices, BIS Working Paper Nr. 692, January. The relationship works based on circularity: monetary policy relies on market prices, and yet monetary policy influences market prices.

See also: K. Forbes, (2016), Uncertainty about Uncertainty, Speech given at the J.P. Morgan Cazenove “Best of British” Conference, November 23. See more in detail: S. Bahaj and A. Foulis, (2016), Macroprudential Policy under Uncertainty, Bank of England Staff Working Paper Nr. 584, January. One of the elements in their mix is ‘presence of unquantifiable sources of risk’ and how to deal with it in the face of uncertainty designing macroprudential policies.

Both in the short-run as known for a while but also long term. It leads to a short-term fall in demand because of precautionary savings and rising markups, as well as creating a fall in productivity. But long term it causes a decline in the main macroeconomic aggregate. Households become averse to these long-term risks affecting their consumption process (long-run risk channel), which strongly exacerbates the precautionary savings motive and the overall negative effects of uncertainty shocks. See in detail: D. Bonciani and J.J. Oh, (2019), The Long run Effects of Uncertainty Shocks, Bank of England Working Paper Nr. 802, June 7.

Macroeconomic forecasts are persistently too optimistic. Forecast errors tend to drive uncertainty shocks; see: P. Chatterjee and S. Nowak, (2016), Forecast Errors and Uncertainty Shocks, IMF Working Paper Series Nr. WP/16/228, November. Also: P. Chatterjee, (2017), Uncertainty Shocks, Financial Frictions and Business Cycle Asymmetries Across Countries, UC Irvine Working Paper, mimeo; N. Bloom, (2014), Fluctuations in Uncertainty, Journal of Economic Perspectives, Vol. 28, Nr. 2, pp. 153–175; K. Jurado, et al., (2015), Measuring Uncertainty, American Economic Review, Vol. 105, Nr. 3, pp. 1177–1216; M. Weale, (2016), What’s Going On? Uncertain Data and Uncertain Outcomes, Speech given at Liverpool University, Bank of England, May 13; H. Armelius et al., (2016), The Timing of Uncertainty Shocks in a Small Open Economy, Sveriges Riksbank Working Paper Series Nr. 334, December. For a country example, see M. Shin et al., (2017), Measuring International Uncertainty: the Case of Korea, Finance and Economics Discussion Series Nr. 66, Washington: Board of Governors of the Federal Reserve System, April; J. Bluedorn and D. Leigh, (2018), Is the Cycle the Trend? Evidence from the Views of International Forecasters, IMF Working Paper Nr. WP/18/163, June. Forecasting in times of crisis is possibly even more difficult but also more relevant. Eicher et al. demonstrate that—based on an examination of 29 macroeconomic variables in terms of bias, efficiency and information content—two-thirds of the key macroeconomic variables that they examine are forecast inefficiently and 6 variables (growth of nominal GDP, public investment, private investment, the current account, net transfers and government expenditures) exhibit significant forecast bias. When they decompose the forecast errors into their sources, they find that forecast errors for private consumption growth are the key contributor to GDP growth forecast errors. Similarly, forecast errors for non-interest expenditure growth and tax revenue growth are crucial determinants of the forecast errors in the growth of fiscal budgets. Forecast errors for balance of payments growth are significantly influenced by forecast errors in goods import growth. See in detail: T.S. Eicher et al., (2018), Forecasts in Times of Crises, IMF Working Paper Nr. WP/18/48.

A.W. Richter and N.A. Throckmorton, (2017), A New Way to Quantify the Effect of Uncertainty, Federal Reserve Bank of Dallas Working Paper Nr. 1705, May 4.

It also pushes out risk-taking to shadow banks. See in detail: X. Tian, (2019), Uncertainty and the Shadow Banking Crisis: Estimates from a Dynamic Model, University of Toronto Working Paper, January, mimeo.

As in general uncertainty can be defined as ‘changes in the variance of the exogenous processes driving the model economy’, macro-uncertainty can be defined as ‘uncertainty about aggregate shocks, such as the time-varying variance of the economy-wide total factor productivity’ and micro-uncertainty as ‘uncertainty about idiosyncratic shocks, such as the cross-sectional dispersion of firm-level productivity in models with heterogeneous firms’. See in detail: A. Cesa-Bianchi and E. Fernandez-Corugedo, (2017), Uncertainty, Financial Frictions and Nominal Rigidities: A Quantitative Investigation, IMF Working Paper Nr. WP/17/211, September, p. 4. See also footnote 3 p. 4 for a full overview of uncertainty literature.

See also: N. Balke et al., (2017), Understanding the Aggregate Effects of Credit Frictions and Uncertainty, Globalization and Monetary Policy Institute Working Paper Nr. 317, Federal Reserve Bank of Dallas; S. Basu and B. Bundick, (2017), Uncertainty Shocks in a Model of Effective Demand, Econometrica, Vol. 85, Issue 3, pp. 937–958.

L. Lohman, (2013), Financialization, Commodification and Carbon: The Contradictions of Neoliberal Climate Change, Working Paper.

T.I. Palley, (2013), Financialization: The Economics of Finance Capital Domination, Palgrave Macmillan, Basingstoke, pp. 31–43, passim.

D.M. Kotz, T. McDonough, and M. Reich (Eds), (1994), Social Structures of Accumulation: The Political Economy of Growth and Crisis, Cambridge University Press, Cambridge, pp. 45–47. See also why the ‘double movement’ Polanyi predicted is not emerging: H. Callaghan, (2015), Who cares About Financialization? Explaining the Decline in Political Salience of Active markets for Corporate Control, MPIfG Discussion paper Nr. 13/4 Max-Planck-Institut for Gesellschaftsordnung, Köln.

D.M. Kotz, (2017), Social Structure of Accumulation Theory, Marxist Theory, and System Transformation, Review of Radical Political Economies, September 14, https://​doi.​org/​10.​1177/​0486613417699050​

D.M. Kotz, (2008), Neoliberalism and Financialization, University of Massachusetts Amherst Working Paper.

Kotz, (2008), Ibid. p. 10. He further illustrates: ‘[t]his is likely the reason why the Rockefellers’ huge fortune, born in oil, was soon shifted to finance and real estate. Chase Manhattan Bank, the Rockefeller bank, was not tied to any particular company or industry’. Regarding workers left assuming the risk, Lin et al. provide compelling evidence to show rising income inequality as a consequence. They argue that the increasing reliance by firms on earnings realized through financial channels decoupled the generation of surplus from production, strengthening owners’ and elite workers’ negotiating power relative to other workers (see in line Piketty (2014), T. Piketty (2014), Capital in the 21st Century, Harvard University Press, Cambridge MA regarding the enhanced bargaining power of corporate managers leading to higher salaries rather than higher levels of productivity). Moreover, the financial conception of the firm reduced capital and management commitment to production, further marginalizing labor’s role in US corporations. The result was an incremental exclusion of the general workforce from revenue generating and compensation setting processes. They (Lin et al.) further suggest that financialization accounts for more than half of the decline in labor’s share of income during the period 1070–2008; see K.-H. Lin and D. Tomaskovic-Devey, (2011), Financialization and U.S. Income Inequality, 1970–2008, University of Massachusetts at Amherst Working Paper and K.-H. Lin and D. Tomaskovic-Devey, (2011), Income Dynamics, Economic Rents and the Financialization of the U.S. Economy, University of Massachusetts at Amherst Working Paper. Similar results for the OECD region were found by: B. Kus, (2013), Financialization and Income Equality in the OECD Region: 1995–2007, University of Massachusetts at Amherst Working Paper.

G. Dumenil and D. Levy, (2005), Costs and Benefits of Neoliberalism, in G. Epstein, (ed.), Financialization and the World Economy, Edward Elgar, Cheltenham and Northampton, p. 19; D. Kotz, and T. McDonough, (2008), Global Neoliberalism and the Contemporary Social Structure of Accumulation, in T. McDonough, M. Reich, and D. Kotz (eds.), Understanding Contemporary Capitalism: Social Structure of Accumulation Theory for the Twenty First Century, Cambridge University Press, Cambridge, pp. 73–83; D. Kotz, (2012), Social Structures of Accumulation, the Rate of Profit, and Economic Crisis, University of Amherst Working Paper and D. Kotz, (2014), The Rise and fall of Neoliberal Capitalism, Harvard University Press, Cambridge MA, in particular Chapter 4.

Financialization also leads to enhanced levels of correlations between different asset classes directly contributing to systemic exposures; See: A. Zaremba, (2013), Implications of Financialization for Strategic Asset Allocation: the Case of Correlations, Poznan University Working Paper.

E. Caverzasi, (2014), Minsky and the Sub-Prime Mortgage Crisis: The Financial Instability Hypothesis in the Era of Financialization, Levy Economics Institute, Working Paper Nr. 796.

K.-H. Lin and D. Tomaskovic-Devey, (2014), Financialization: Causes, Inequality Consequences, and Policy Implications, University of Amherst Working Paper.

Referring to the book written about the Leveraged Buy-Out (LBO) of RJR Nabisco, which has become iconic for predatory capitalism and avarice; See:, J. Heylar and B. Burrough, (1989), HarperCollins, New York.

When it comes to the reduction of manufacturing in the West the most frequent explanations for this decline are productivity gains and increased trade with low-wage economies. Pressures came from the financial markets to offload activities that sustain manufacturing and depleted the manufacturing ecosystem. See in detail the MIT Project ‘Production in the Innovative Economy’ and S. Berger, (2014), How Finance Gutted Manufacturing, Boston Review April 1.

A. Demirgüç-Kunt, E. Feyen, and R. Levine, (2011), The Evolving Importance of Banks and Securities Markets, World Bank, Policy Research Working Paper, Nr. 5805.

L. Gambacorta, J. Yang, and K. Tsatsaronis, (2014), Financial Structure and Growth, BIS Quarterly Review, March 2014, pp. 21–35.

S. Cecchetti and E. Kharroubi, (2012), Reassessing the Impact of Finance on Growth, BIS Working Papers, Nr. 381.

S. Law and N. Singh, (2014), Does Too Much Finance Harm Economic Growth?, Journal of Banking and Finance, Vol. 41, pp. 36–44.

See, for instance, on capital controls: C. Saborowski, S. Sanya, H. Weisfeld, and J. Yepez, (2014), Effectiveness of Capital Outflow Restrictions, IMF Working Paper, WP/14/8; Juan Pablo Medina and Jorge Roldós, (2014), Monetary and Macroprudential Policies to Manage Capital Flows, IMF Working Paper, WP/14/30.

R. McCauley, P. McGuire and G. von Peter, (2010), The Architecture of Global Banking: from International to Multinational, BIS Quarterly Review, March 2010, pp. 25–37; R. De Haas and I. van Lelyveld, (2010), Internal Capital Markets and Lending by Multinational Bank Subsidiaries, Journal of Financial Intermediation, Vol. 19, pp. 1–25. Internal capital markets in business groups can propagate corporate shareholders’ credit supply shocks to their subsidiaries. Equity exchanges are one channel through which corporate shareholders transmit bank credit supply shocks to the subsidiaries. For example, one study documents that an average of 16.7% local bank credit growth where corporate shareholders are located would increase subsidiaries investment by 1% of their tangible fixed asset value. See in detail: Y. Shi et al., (2019), Internal Capital Markets in Business Groups and the Propagation of Credit Supply Shocks, IMF Working Paper nr. WP/19/111, May. Also: L. Alfaro et al., (2019), On the Direct and Indirect Real Effects of Credit Supply Shocks, NBER Working Paper Nr. 25458, January. Credit supply shocks have sizable direct and downstream propagation effects on investment and output throughout the period, the latter conclude. Also: L. Alfaro et al., (2018), On the Direct and Indirect Effects of Credit Supply Shocks, Bank of Spain Working Paper Nr. 9; A. Cesa-Bianchi et al., (2017), International Credit Supply Shocks, Bank of England Staff Working Paper Nr. 680, October 4. House prices and exchange rates can potentially amplify the expansionary effect of capital inflows by inflating the value of collateral.

J. Blouin, H. Huizinga, L. Laeven and G. Nicodème, (2014), Thin Capitalization Rules and Multinational Firm Capital Structure, IMF Working Paper, WP/14/12.

S. Claessens and L. Kodres, (2014), The Regulatory Responses to the Global Financial Crisis: Some Uncomfortable Questions, IMF Working Paper, WP/14/46.

T. Tressel and T. Verdier, (2014), Optimal Prudential Regulation of Banks and the Political Economy of Supervision, IMF Working Paper, WP/14/90. Low interest rates lead to enhanced inequality as the affluent hold more equity positions relative to the non-affluent part of society and therefore benefit disproportionately more, see: McKinsey, (2013), QE and Low Interest Rates: Distributional Effects and Risks, Working Paper.

R. Mohan and M. Kapur, (2014), Monetary Policy Coordination and the Role of Central banks, IMF Working Paper, WP/14/70.

C. Minoiu, C. Kang, V.S. Subrahmanian, and A. Berea, (2013), Does Financial Connectedness Predict Crisis, IMF Working Paper, WP/13/267.

L. Laeven, L. Ratnovski and H. Tong, (2014), Bank Size and Systemic Risk, IMF Staff Discussion Note, SDN/14/04. For a full and updated review of systemic banking crises including information on crisis dates, policy responses to resolve banking crises, and the fiscal and output costs of crises see L. Laeven and F. Valencia, (2018), Systemic Banking Crises Revisited, IMF Working Paper Nr. WP/18/206, September, in particular pp. 12–23. The authors conclude that crises in high-income countries tend to last longer and be associated with higher output losses, lower fiscal costs, and more extensive use of bank guarantees and expansionary macro policies than crises in low- and middle-income countries. Also: M. Baron, (2018), Identifying Banking Crises, Princeton University Working Paper, Mimeo; D. Romer and C. Romer, (2017), New Evidence on the Aftermath of Financial Crises in Advanced Countries, American Economic Review, Vol. 107, Issue 10, pp. 3072–3118. Lorenc and Zhang conclude that stress experienced by banks in the top 1% of the size distribution leads to a statistically significant and negative impact on the real economy. This impact increases with the size of the bank. The negative impact on quarterly real GDP growth caused by stress at banks in the top 0.15% of the size distribution is more than twice as large as the impact caused by stress at banks in the top 0.75%, and more than three times as large as the impact caused by stress at banks in the top 1%. These results are broadly informative as to how the stringency of regulatory standards should vary with bank size, and support the idea that the largest banks should be subject to the most stringent requirements while smaller banks should be subject to successively less stringent requirements. In detail: A.G. Lorenc and J.Y. Zhang, (2018), The Differential Impact of Bank Size on Systemic Risk, Finance and Economics Discussion Series 2018-066. Washington: Board of Governors of the Federal Reserve System, https://​doi.​org/​10.​17016/​FEDS.​2018.​066

R. Babihuga and M. Spaltro, (2014), Bank Funding Costs for International Banks, IMF Working Paper, WP/14/71. Regarding the impact of Monetary Policy and Funding Costs see: M. Girotti, (2016), How Monetary Policy Changes Bank Liability Structure and Funding Cost, Banque de France Working Paper Nr. 590, April, Paris; S. Schmitz et al., (2017), Bank Solvency and Funding Cost: New Data and New Results, IMF Working Paper Nr. WP/17/116, May.

See the findings of an analysis in this matter: J. Viňals, C. Pazarbasioglu, J. Surti, A. Narain, M. Erbenova, and J. Chow, (2013), Creating a Safer Financial System: Will the Volcker, Vickers, and Liikanen Structural Measures Help?, IMF Staff Discussion Note, SDN/13/4. See for an evaluation of the Volcker rule from a systemic risk point of view: J.A.D. Manasfi, (2013), Systemic Risk and Dodd-Frank’s Volcker Rule, William and Mary Business Law Review, Vol. 4, Issue 1, pp. 181–212, February. Also: M. Bitar et al., (2017), Basel Compliance and Financial Stability: Evidence from Islamic Banks, IMF Working Paper Nr. WP/17/161, July. Bitar et al. demonstrate that compliance with Basel standards reduces the Z-score (which forecasts bankruptcy) of conventional banks as well as Islamic banks (but to a leser degree).

N. Xingyuan Che and Y. Shinagawa, (2014), Financial Soundness Indicators and the Characteristics of Financial Cycles, IMF Working Paper, Nr. WP/14/14.

Ph. Aghion and E. Kharroubbi, (2013), Cyclical Macroeconomic Policy, Financial Regulation and Economic Growth, BIS Working Paper Nr. 434.

A. Pescatori, D. Sandri, and J. Simon, Debt and Growth: Is There a Magic Threshold, IMF Working Paper, Nr. WP/14/34. Also: B. Jiang and T.S. Sedik, (2019), The Turning Tide: How Vulnerable are Asian Corporates?, IMF Working Paper Nr. WP/19/93, May. Higher global interest rates and exchange rate depreciation increase the probability of default of Asian firms. See also their literature list pp. 16–17 for further analysis and additional dimensions.

B. Öztürk and M. Mrkaic, (2014), SMEs’ Access to Finance in the Euro Area: What Helps or Hampers?, IMF Working Paper, Nr. WP/14/78 and N. Klein, (2014), Small and Medium Size Enterprises, Credit Supply Shocks, and Economic Recovery in Europe, IMF Working Paper, Nr. WP/14/98. Credit supply shocks have sizable direct and downstream propagation effects on investment and output also, conclude Alfaro et al. in L. Alfaro et al., (2018), On the Direct and Indirect Real Effects of Credit Supply Shocks, Working Paper, July 24, mimeo. Wider confirmation can be found in: M. Bottero et al., (2017), Sovereign Debt Exposure and the Bank Lending Channel: Impact on Credit Supply and the Real Economy, Working paper, mimeo, December and F. Manaresi and Nicola Pierri, (2018), Credit Supply and Productivity Growth, BIS Working Paper Nr. 711, Monetary and Economic Department, March.

S. Cevik and K. Teksoz, (2014), Deep Roots of Fiscal Behavior, IMF Working Paper, Nr. WP/14/45.

S.G. Cecchetti and E. Kharroubi, (2012), Re-assessing the Impact of Finance on Growth, BIS Working Paper, Nr. 381 and S.G. Cecchetti and E. Kharroubi, (2013), Why Does Financial Sector Growth Crowd Out Real Economic Growth?, BIS Working Paper; S.H. Law and N. Singh, (2013), Does Too Much Finance Harm Economic Growth, Working Paper. They demonstrate similar findings. Cecchetti et al. draw two important conclusions: ‘[f]irst, the growth of a country’s financial system is a drag on productivity growth. That is, higher growth in the financial sector reduces real growth. In other words, financial booms are not, in general, growth enhancing, probably because the financial sector competes with the rest of the economy for resources. Second, they examined the distributional nature of this effect and find that credit booms harm what we normally think of as the engines for growth: those that are more R&D-intensive.’ Their findings point to a pressing need to reassess the relationship of finance and real growth in modern economic systems. Cecchetti and Kharroubi expanded their study in 2015: (2015), Why Does Financial Sector Growth Crowd out Real Economic Growth, BIS Working Paper Nr. 490, February. They identify which parts of the real economy are hurt most by financial outcrowding (R&D intensive, high asset/low productivity sectors), that after they concluded that financial growth in the West reduced real growth overall and financial booms don’t trickle down and generate real economic growth. The financial sector often competes with the other real sectors for resources. This also has a globalization dimension. See: S. Chen and E. Dauchy, (2018), International Technology Sourcing and Knowledge Spillovers; Evidence from OECD Countries, IMF Working Paper Nr. WP/18/51, March and J.L. Eugster et al., (2018), International Knowledge Spillovers, IMF Working Paper nr. WP/18/269, December. The authors highlight: ‘[f]or all countries, and especially for emerging economies, inflows of foreign knowledge have a growing and quantitatively important impact on domestic innovation.’

See for an excellent analysis including the policy actions taken post-2008 crisis: M. Mazzucato and L.R. Wray, (2015), Financing the Capital Development of the Economy. A Keynes-Schumpeter-Minsky Synthesis, Levy Economics Institute Working Paper Nr. 837, May. They do not only conclude that most of the financial innovation was direct outside the sphere of production, but also the real economy itself has retreated from funding investment opportunities and is instead either hoarding cash or using corporate profits for speculative investments such as share buybacks. It is therefore not just a matter of runaway finance and an investment-starved real economy. The real economy is part of that momentum.

S. Dées and J. Güntner, (2014), The International Dimensions of Confidentiality Shocks, ECB Working Paper Series, Nr. 1669.

C. O’Neill and N. Vause, (2018), Macroprudential margins: A New Countercyclical Too? Bank of England Staff Working Paper Nr. 765, November 9. Macroprudential margins to eliminate fire-sale externalities can work but not in all sorts of market conditions and only when introduced ex ante.

T. Ahnert, (2014), Rollover Risk, Liquidity and Macro-Prudential Regulation, ECB Working Paper Series, Nr. 1667; V.V. Acharya and H. Naqvi, (2012), The Seeds of a Crisis: A Theory of Bank Liquidity and Risk-Taking over the Business Cycle, CEPR Discussion Papers Nr. 8851.

D. Tomaskovic-Devey et al., (2015), Did Financialization Reduce Economic Growth, Amherst Working Paper, University of Massachusetts, Mimeo. The impact of financialization goes way beyond reduced value add, but impact economies and entrepreneurship and entrepreneurial intent at its core; see: P. Kedrosky and D. Stangler, (2011), Financialization and Its Entrepreneurial Consequences, Kauffman Foundation Research Series: Firm Formation and Economic Growth, March, Mimeo: an orientation toward shareholder value has led to substantial changes in corporate strategies and structures that have encouraged outsourcing and corporate disaggregation while increasing compensation at the top. Second, financialization has shaped patterns of inequality, culture and social change in the broader society. Underlying these changes is a broad shift in how capital is intermediated, from financial institutions to financial markets, through mechanisms such as securitization (turning debts into marketable securities). Enabled by a combination of theory, technology and ideology, financialization is a potent force for changing social institutions; see: G.F. Davis and S. Kim, (2015), Financialization of the Economy Annual Review of Sociology, Vol. 41, pp. 203–221.

BCBS, (2014), Review of the Pillar 3 Disclosure Requirements- Consultative Document, June 24. Final version: January 28, 2015 via bis.​org

(Capital) Income taxes become problematic the more the economies they cover become open economies, which is the case in terms of globalization as the elasticity of capital income taxes becomes larger as economies become open structured, see: E. Mendoza, L. Tesar and J. Zhang, (2014), Saving Europe: The Unpleasant Arithmetic of Fiscal Austerity in Integrated Economies, NBER Working Paper, Nr. 20200.

See in detail also for the quantification of the externalities involved: B. Lockwood, C. Nathanson, and E.G. Weyl, (2014), Taxation and the Allocation of Talent, Harvard University Working Paper. Similar dynamics can be identified with respect to marketing and the negative externalities it causes in terms of overconsumption, pollution and debt, see: S. Steed and H. Kersley, (2014), A Bit Rich: Calculating the Real Value to Society of Different Professions, NEF Working Paper, mimeo.

See for example the application of Pigovian taxes in tourism: C. Piga, (2003), Pigouvian Taxation in Tourism, Environmental and Resource Economics, Vol. 26, pp. 343–359.

See: C. Todd, S. Kallbekken and S. Kroll, (2011), The Impact of Trial Runs on the Acceptability of Pigouvian Taxes: Experimental Evidence, Cicero Working Paper, Nr. 2011/01.

See how shadow banking is co-defined by culture and cultural differences: A. Wolter, (2016), Shadow Banking a Cultural Phenomenon? Cultural Dimensions as by Hofstede and others, Master Thesis, Radboud Universiteit Nijmegen, October, mimeo. She uses the six dimensions of culture as developed by Hofstede (chapter four). There is more financial behavior defined by culture. See B. Guin, (2017), Culture and Household Saving, (2017), ECB Working Paper Nr. 2069, May. The German-speaking parts of Switzerland save materially more than the French-speaking part adjusted for all variables.

Even domestic applications of Pigovian instruments, despite their natural limitations, have the potential to spur regional or even global applications, given the spillover effects that exist in international taxation; see: IMF Policy Paper, (2014), Spillovers in International Corporate Taxation, May 9.

M. McCarthy, (2014), Neoliberals without the Neoliberals, Evidence from the Rise of 401(k) Retirement Plans, MPIfG Discussion Paper 14/12, Max Planck Institute for the Study of Societies, Cologne.

Although the first is still the dominant line of thinking across the political spectrum and at the level of international institutions (EU, IMF, etc.) when judged based on the conditions they imposed on countries receiving emergency funding.

See chapter two of L. Nijs, (2015), Ibid. for details on their individual contributions to the neoliberal program.