2. Shadow Banking Around the Globe

S. Samuels, (2015), Withering Regulation Will Make for Shrivelled Banks, Financial Times, January 13.

B. Holstrom, (2015), Understanding the Role of Debt in the Financial System, BIS Working Paper Nr. 479.

Referring here to the 2007/2008 crisis for those confused about how many crises we have had in the last half a decade.

S. Fleming, (2014), Shadow Banking Nears Pre-crisis Peak, Financial Times, October 30.

S. Das, (2015), Risks Lure in Failure to Simplify Finance, Financial Times, January 8.

FSB, (2014), Global Shadow Banking Monitoring Report, p. 38.

For example, ‘restriction on lending or deposit rates’.

FSB, (2014), Global Shadow Banking Monitoring Report, p. 39.

See in detail: IMF, (2014), Global Financial Stability Report (October). Risk Taking, Liquidity and Shadow Banking. Curbing Excess While Promoting Growth, World Economic and Financial Surveys; chapter 2, Shadow Banking Around the Globe: How Large and How Risky?, pp. 65–105 but in particular pp. 81–86.

For an illustration of the operations of the traditional versus shadow banking market, see IMF, Global Financial Stability Report, Figure 2.3, p. 69.

See extensively o.a. T. Adrian, (2014), Financial Stability Policies for Shadow Banking, FRBNY Staff Reports Nr. 664, Federal Reserve Bank of New York.

See in detail: T. Adrian, et al., (2013), Shadow Bank Monitoring, FRBNY Staff Report Nr. 638, Federal Reserve Bank of New York.

R. Caballero and A. Simsek, (2009), Complexity and Financial Panics, NBER Working Paper Nr. 14,997, National Bureau of Economic Research, Cambridge, Massachusetts.

M. Brunnermeier and L. H. Pedersen, (2009), Market Liquidity and Funding Liquidity, Review of Financial Studies, Vol. 2, issue 6, pp. 2201–2238. See also the discussion regarding the risks involved in securities lending and repos elsewhere in the book. See extensively: FSB, (2013), Policy Framework for Addressing Shadow Banking Risks in Securities Lending and Repos.

See in detail and for illustrations: IMF, (2014), Global Financial Stability Report, pp. 60–61.

Z. Pozsar et al., (2013), Shadow Banking, Economic Policy Review, Vol. 19, Issue 2, pp. 1–16.

See in detail: B. Holmström, (2015), Understanding the Role of Debt in the Financial System, BIS Working Paper Nr. 479.

See a contrario: R. Gilson and R. Kraakman, (2014), Market Efficiency After the Financial Crisis: It’s Still a Matter of Information Costs, Virginia Law Review, Vol. 100, Issue 2, pp. 313–375. They advocate that transparency requires market discipline and provide a warning indicator about impending buildup of systemic risk.

B. Holmström, (2015), ibid., p. 3. He advocates his case by going back to the origins of the debt markets, that is, the Chinese pawnshops during the Tang Dynasty (pp. 3–4).

See in detail B. Holmström, (2015), ibid., pp. 4–7. To be precise: ‘risk-sharing versus liquidity/funding provision, price discovery versus no price discovery, information-sensitive versus insensitive, transparent versus opaque, large versus small investments in information, anonymous versus bilateral, small unit trades versus large unit trades and collateralized versus unsecured’ (pp. 7 and 35). Each of these elements makes the debt/stock market two coherent but separate systems that are carefully designed.

B. Holmström, (2015), ibid., p. 5.

Holmström makes a comparison with the initial Chinese pawnshop situation: ‘[i]t did not matter that the pawnbroker’s valuation of the watch was different from the borrower’s. It sufficed that the pawn broker felt confident that he could recover the loan by selling the watch, while the borrower was protected by the right to redeem the watch’ (p. 5).

See for an application in the case of securitization: M. Pagano and P. Volpin, (2012), Securitization, Transparency and Liquidity, Review of Financial Studies, Vol. 25, Issue 8, pp. 2417–2453.

The much-acclaimed Costly State Verification (CSV) model developed decades ago supports this finding. They argue that default can usefully be thought of as contingent price discovery. See in detail: D. Gale and M. Hellwig, (1985), Incentive Compatible Debt Contracts: The One-Period Problem, Review of Economic Studies, Vol. 52, Issue 4, pp. 647–663; R. Townsend, (1979), Optimal Contracts and Competitive Markets with Costly State Verification, Journal of Economic Theory, Vol. 21, pp. 265–293.

The model built around that understanding can be found with: T.V. Dang et al., (2012), Ignorance, Debt and the Financial Crisis, mimeo, Yale University. Their main finding is that less-risky collateral makes debt less information-sensitive.

‘If IAS is larger than the cost of information acquisition, the buyer will acquire information; if IAS is smaller the cost of information acquisition, she will refrain from acquiring information’ (Holmström, [2015], ibid., p. 10). For a discussion on how the IAS changes as the underlying parameters change, see Holmström, (2015), ibid., pp. 10–12.

See also: P. DeMarzo P, et al., (2005), Bidding with Securities: Auctions and Security Design, American Economic Review, Vol. 95, Issue 4, pp. 936–958.

Holmström, (2015), ibid., p. 12. Banks should therefore hold (or would benefit most from) low-risk assets and secrecy tends to be beneficial in banking relations. See in detail: T.V. Dang et al., (2014), Banks as Secret Keepers, mimeo, Yale University and R. Breton, (2007), Monitoring and the Acceptability of Money, mimeo, Centre national de la recherche scientifique.

See for why transparency could have negative effects in the (shadow) banking sector: Holmström, (2015), ibid., pp. 13–15. Those could include ‘preventing adverse selection’ and ‘alleviating the winner’s curse’ and why purposeful opacity can enhance liquidity: (1) opacity provides the time to adjust to fluctuations in daily net asset value (NAV); (2) coarseness of bond ratings makes approximately equal collateral look equal (‘commonality reduces adverse selection’) in the eyes of investors; and (3) money markets are symmetrically ignorant providing stability.

See for a wide variety of research on this matter as listed by Holmström, (2015), ibid., p. 13.

See for the initial position: J. Hirshleifer, (1971), The Private and Social Cost of Information and the Reward to Inventive Activity, American Economic Review, Vol. 61, Issue 4, pp. 561–574.

G. Gorton and G. Ordonez, (2014), Collateral Crises, American Economic Review, Vol. 104, issue 2, pp. 343–378.

See for evidence of that position: W. Perraudin and S. Wu, (2008), Determinants of Asset-Backed Security Prices in Crisis Periods, Research Paper, Nr. 8/3, RiskControl, London. They demonstrate that during panics significant new public information caused beliefs to diverge rather than converge to a common, lower price level.

See: D. Covitz, (2013), The Evolution of a Financial Crisis: Collapse of the Asset-Backed Commercial Paper Market, Journal of Finance, Vol. 68, Issue 3, pp. 815–848, and M. Brunnermeier, (2009), Deciphering the Liquidity and Credit Crunch 2007–08, Journal of Economic Perspectives, Vol. 23, Issue 1, pp. 77–100.

See in case of the repo market: A. Martin et al., (2014), Repo runs, Journal of Finance, Vol. 69, Issue 6, pp. 2343–2380.

Holmström, (2015), ibid., pp. 17–19.

See in detail: R. Caballero, (2009), The other Imbalance and the Financial Crisis, Pablo Baffi Lecture, and R. Caballero et al., (2008), An Equilibrium Model of Global Imbalances and Low Interest Rates, American Economic Review, Vol. 98, Issue 1, pp. 358–393.

Holmström, (2015), ibid., p. 20.

See in detail: A. Krishnamurthy, et al., (2014), Sizing up repo, Journal of Finance, Vol. 69, issue 6, pp. 2381–2417, and A. Copeland et al., (2014), Repo Runs: Evidence from the Tri-Party Repo Market, Staff Report, Nr. 506, Federal Reserve Bank of New York.

See in detail: J. Geanakoplos, (2010), The Leverage Cycle, in D. Acemoglu, K. Rogoff and M. Woodford (eds.), NBER Macroeconomics Annual 2009, National Bureau of Economic Research; A. Rampini and S. Viswanathan (2010), Collateral, Risk Management and the Distribution of Debt Capacity, Journal of Finance, Vol. 65, pp. 2293–2322; and B. Holmström and J. Tirole, (2011), Inside and Outside Liquidity, MIT Press, Cambridge, MA.

Holmström, (2015), ibid., p. 22.

Holmström, (2015), ibid., p. 22.

I. Goldstein and H. Sapra, (2013), Should Banks’ Stress Tests Be Disclosed? An Analysis of the Costs and Benefits, mimeo, The Wharton School, University of Pennsylvania; T. Schuermann, (2014), Stress Testing Banks, International Journal of Forecasting, Vol. 30, Issue 3, pp. 717–728.

See J. Viñals, (2014), The New Global Imbalance: Too Much Financial Risk-Taking, Not Enough Economic Risk-Taking, IMFdirect, October 8.

J. Viñals, (2014), ibid.

S. Ghosh et al., (2012), Chasing the Shadows: How Significant Is Shadow Banking in Emerging Markets? Economic Premise, World Bank Note Series, Vol. 88, pp. 1–7.

S. Claessens et al., (2012), Shadow Banking: Economics and Policy, IMF Staff Discussion Note SDN/12/12, International Monetary Fund, Washington.

The two most dominant reports regarding this measure are (and which are discussed elsewhere, although in different parts of the book): (1) H. S. Shin, (2010), Macro-Prudential Policies Beyond Basel III, Policy Memo, Princeton University, Princeton, New Jersey, and (2) A. Harutyunyan et al., (2015), Shedding Light on Shadow Banking, IMF Working Paper, Nr. WP/15/1, International Monetary Fund, Washington. See also: IMF, (2014), Global Liquidity – Key Aspects for Surveillance, IMF Policy Paper, Washington, March. ‘A narrow measure of noncore liabilities excludes those confined to the financial sector; it is thus a proxy for the intermediation between ultimate lenders and ultimate borrowers—that is, between the financial sector and the real economy. The difference between the broad and narrow measures represents an estimate of the amount of credit intermediation conducted within the shadow banking sector’ (IMF, [2014], Global Financial Stability Report, ibid., October, pp. 72–73). The different measurements yield different results as can be compared through table 2.1 (p. 73).

The other methods therefore exclude them as well as some other studies: for example, K. Bakk-Simon et al., (2012), Shadow Banking in the Euro Area: An Overview, ECB Occasional Paper, Nr. 133, European Central Bank, Frankfurt and T. Adrian, (2012), Shadow Banking: A Review of the Literature, FRBNY Staff Report Nr. 580, Federal Reserve Bank of New York.

Office of Financial Research (OFR), 2013, Asset Management and Financial Stability, OFR Report, U.S. Department of the Treasury, Washington, and M. Feroli et al. (2014), Market Tantrums and Monetary Policy, Chicago Booth Research Paper 14–09, Initiative on Global Markets, University of Chicago Booth School of Business, Chicago.

See in detail: IMF, (2014), Risk Taking, Liquidity and Shadow Banking, ibid., pp. 31–41.

IMF, (2014), ibid., pp. 2 and 7 (Figure 1.6).

See in detail: IMF, (2014), ibid., pp. 16–19.

See in detail: IMF, (2014), ibid., pp. 21–31.

See for the impact of recent financial regulation of banks and their activities; IMF, (2014), ibid., p. 59 (Table 1.7).

IMF, (2014), ibid., p. 43. For an in-depth analysis of the trade-off between the benefits of monetary accommodation in support of economic activity and balance sheet repair, and the downside risks associated with financial excesses that could, if they become systemic, pose risks to the real economy, see IMF, (2014), ibid., pp. 41–48.

IMF, (2014), ibid., pp. 45–48.

See in detail: (1) P. Jackson, (2013), Shadow Banking and New Lending Channels—Past and Future, in 50 Years of Money and Finance: Lessons and Challenges, Vienna: The European Money and Finance Forum; (2) T. Goda, et al., (2013), The Contribution of U.S. Bond Demand to the U.S. Bond Yield Conundrum of 2004 to 2007: An Empirical Investigation, Journal of International Financial Markets, Institutions and Money, Vol. 27, pp. 113–136; (3) T. Goda and Ph. Lysandrou, (2014), The Contribution of Wealth Concentration to the Subprime Crisis: A Quantitative Estimation, Cambridge Journal of Economics, Vol. 38, Issue 2, pp. 301–327; (4) Ph. Lysandrou, (2012), The Primacy of Hedge Funds in the Subprime Crisis, Journal of Post Keynesian Economics, Vol. 34, Issue 2, pp. 225–253.

P. Mehrling, et al., (2013), Bagehot Was a Shadow Banker: Shadow Banking, Central Banking, and the Future of Global Finance, Shadow Banking Colloquium, a project of the Financial Stability Research Program of the Institute for New Economic Thinking as well as the aforementioned H.S. Shin, (2010), Macro-prudential Policies Beyond Basel III, Policy Memo, Princeton University, Princeton, New Jersey.

See in extenso: (1) D. Vittas, (ed.), (1992) Financial Regulation—Changing the Rules of the Game, Washington, World Bank Publications; (2) G. Kanatas and S. I. Greenbaum, (1982), Bank Reserve Requirements and Monetary Aggregates, Journal of Banking and Finance, Vol. 6, Issue 4, pp. 507–520; (3) G.F. Udell and A. N. Berger, (1994), Did Risk-Based Capital Allocate Bank Credit and Cause a Credit Crunch in the U.S.?, Journal of Money, Credit and Banking, Vol. 26, Issue 3, pp. 585–628; (4) J.V. Duca, (1992), U.S. Business Credit Sources, Demand Deposits, and the Missing Money, Journal of Banking and Finance, Vol. 16, Issue 3, pp. 567–583; (5) J.V. Duca, (2014), What Drives the Shadow Banking System in the Short and Long Run? FRB Dallas Working Paper, Nr. 1401, Federal Reserve Bank of Dallas, Dallas; and (6) B.S. Bernanke, and C.S. Lown, (1991), The Credit Crunch, Brookings Papers on Economic Activity, Vol. 2, pp. 204–239.

See in detail (also discussed extensively elsewhere in the book): Z. Pozsar, (2011), Institutional Cash Pools and the Triffin Dilemma of the U.S. Banking System, IMF Working Paper, Nr. WP/11/190, International Monetary Fund, Washington.

IMF, (2014), ibid., p. 75, footnote 13.

G.A. Akerlof, et al. (Eds.), (2014), What Have We Learned?, Macroeconomic Policy After the Crisis, MIT Press, Cambridge, MA.

See extensively: X. Freixas, et al., (2015), Systemic Risk, Crises, and Macroprudential Regulation, MIT Press, Cambridge, MA.

S. Claessens, (2014), An Overview of Macro-Prudential Policy Tools, IMF Working Paper, WP/14/214, p. 3.

That is, caps on loan-to-value ratios, limits on credit growth, additional capital adequacy requirements, reserve requirements and other balance sheets restrictions.

See G. Galati and R. Moessner, (2014), What do we know about the effects financial macro-prudential policy?, De Nederlandse Bank Working Papers, Nr. 440, and historically: D.J. Elliott, (2013), The History of Cyclical Macroprudential Policy in the United States, Office of Financial Research Working Paper Nr. 8 (Washington: US Department of the Treasury).

Claessens, (2014), ibid., p. 5.

See for a number of them: (1) C. E.V. Borio, (2003), Towards a Macroprudential Framework for Financial Supervision and Regulation? BIS Working Paper Nr. 128, Bank for International Settlements, Basel; (2) C.E.V. Borio, Claudio, and W. R. White, (2003), Whither Monetary and Financial Stability: The Implications of Evolving Policy Regimes?, Monetary Policy and Uncertainty: Adapting to a Changing Economy, Conference Volume, Federal Reserve Bank of Kansas City; and (3) W.R. White, (2006), Procyclicality in the Financial System: Do We Need a New Macrofinancial Stabilisation Framework?, BIS Working Papers, Nr. 193, Basel.

See in detail: (1) M.K. Brunnermeier, (2009), The Fundamental Principles of Financial Regulation: 11th Geneva Report on the World Economy, CEPR/ICMB; (2) G. De Nicolò et al., (2012), Externalities and Macroprudential Policy, IMF Staff Discussion Notes, Nr. 12/05; (3) F. Allen and E. Carletti, (2011), Systemic Risk and Macroprudential Regulation, mimeo, University of Pennsylvania; (4) Bank of England, (2011), Instruments of Macroprudential Policy, Discussion Paper, December; and (5) D. Schoenmaker, and P.J. Wierts, (2011), Macroprudential Policy: The Need for a Coherent Policy Framework, DSF Policy Paper Nr. 13, Duisenberg School of Finance, Amsterdam, the Netherlands.

See supra; cited and discussed in Claessens, (2014), ibid., pp. 5–8.

See in detail: (1) M. Ruckes, (2004), Bank Competition and Credit Standards, Review of Financial Studies, Vol. 17, Issue 4, pp. 1073–1102; (2) G. Dell’Ariccia and R. Marquez, (2006), Lending Booms and Lending Standards, Journal of Finance, Vol. 61, Issue 5, pp. 2511–2546; (3) G.B. Gorton and P. He, (2008), Bank Credit Cycles, Review of Economic Studies, Vol. 75, issue 4, pp. 1181–1214; (4) R.G. Rajan, (1994), Why Bank Credit Policies Fluctuate: A Theory and Some Evidence, The Quarterly Journal of Economics, Vol. 109, Issue 2, pp. 399–441; (5) V.V. Acharya, (2013), Seeking Alpha: Excess Risk Taking and Competition for Managerial Talent, NBER Working Papers Nr. 18,891, National Bureau of Economic Research; (6) L. Allen and A. Saunders, (2003), A Survey of Cyclical Effects in Credit Risk Measurement Models, BIS Working Papers Nr. 126, Bank for International Settlements, Basel; (7) T. Adrian and H. S. Shin, (2010), Liquidity and Leverage, Journal of Financial Intermediation, Elsevier, Vol. 19, Issue 3, pp. 418–437; (8) T. Adrian and H. S. Shin, (2014), Procyclical Leverage and Value-at-Risk, Review of Financial Studies, Vol. 27, Issue 2, pp. 373–403; (9) N. Barberis, (2013), Thirty Years of Prospect Theory in Economics: A Review and Assessment, Journal of Economic Perspectives, Vol. 27, pp. 173–196; (10) N. Gennaioli, et al., (2013), A Model of Shadow Banking, Journal of Finance, Vol. 68, issue 4, pp. 1331–1363; (11) E. Farhi and J. Tirole, (2012), Collective Moral Hazard, Maturity Mismatch, and Systemic Bailouts, American Economic Review, Vol. 102, Issue 1, pp. 60–93; (12) F. Allen and E. Carletti, (2011), Systemic Risk and Macroprudential Regulation, mimeo, University of Pennsylvania; and (13) L. Ratnovski, (2009), Bank Liquidity Regulation and the Lender of Last Resort, Journal of Financial Intermediation, Vol. 18, Issue 4, pp. 541–588.

See in detail: (1) F. Allen and D. Gale, (1994), Limited Market Participation and Volatility of Asset Prices, American Economic Review, Vol. 84, Issue 4, pp. 933–955; (2) A. Shleifer and R. W. Vishny, (1992), Liquidation Values and Debt Capacity: A Market Equilibrium Approach, Journal of Finance, Vol. 47, Issue 4, pp. pp. 1343–1366; (3) R. G. Rajan and R. Ramcharan, (2014), Financial Fire Sales: Evidence from Bank Failures, Finance and Economics Discussion Series, 2014–67, Federal Reserve Board; (4) I. Goldstein, et al., (2013), Trading Frenzies and Their Impact on Real Investment, Journal of Financial Economics, Vol. 109, issue 2, pp. 566–582; (5) A. Korinek, 2014, Global Coordination or Currency Wars?, unpublished; Baltimore, Maryland, Johns Hopkins University; (6) S. Schmitt-Grohe and M. Uribe, (2012), Prudential Policy for Peggers, NBER Working Papers Nr. 18,031, National Bureau of Economic Research; (7) E. Farhi and I. Werning, (2013), A Theory of Macroprudential Policies in the Presence of Nominal Rigidities, NBER Working Papers Nr. 19,313, National Bureau of Economic Research; (8) M. Brunnermeier, et al., (2013), Macroeconomics with Financial Frictions: A Survey, Advances in Economics and Econometrics, Tenth World Congress of the Econometric Society, New York, Cambridge University Press; (9) A. Manconi, et al., (2012), The Role of Institutional Investors in Propagating the Crisis 2007–2008, Journal of Financial Economics, Vol. 104, Nr. 3, pp. 491–518; (10) C.B. Merrill, et al., (2012), Did Capital Requirements and Fair Value Accounting Spark Fire Sales in Distressed Mortgage-Backed Securities?, NBER Working Paper Nr. 18,270; (11) A. Manconi, et al., (2012), The Role of Institutional Investors in Propagating the Crisis 2007–2008, Journal of Financial Economics, Vol. 104, Nr. 3, pp. 491–518; (12) J. Bianchi, (2010), Credit Externalities: Macroeconomic Effects and Policy Implications, American Economic Review, Vol. 100, Issue 2, pp. 398–402; and (13) C. Merrill, et al., (2012), Did Capital Requirements and Fair Value Accounting Spark Fire Sales in Distressed Mortgage-Backed Securities?, NBER Working Paper Nr. 18,270.

See in detail: (1) F. Allen and D. Gale, (2000), Financial Contagion, Journal of Political Economy, Vol. 108, Issue 1, pp. 1–33; (2) D.W. Diamond and R. G. Rajan, (2011), Fear of Fire Sales, Illiquidity Seeking, and the Credit Freezes, Quarterly Journal of Economics, Vol. 126, Issue 2, pp. 557–591; (3) E. Perotti and J. Suarez, (2011,) A Pigovian Approach to Liquidity Regulation, International Journal of Central Banking, Vol. 7, Issue 4, pp. 3–41; (4) L. Bebchuk and I. Goldstein, (2011), Self-Fulfilling Market Freezes, Review of Financial Studies, Vol. 24, Isue 11, pp. 3519–3555; (5) D. Acemoglu, et al., (2013), Systemic Risk and Stability in Financial Networks, NBER Working Papers Nr. 18,727, National Bureau of Economic Research (published as D. Acemoglu, (2015), Systemic Risk and Stability in Financial Networks, American Economic Review, Vol. 105, Issue 2, pp. 564–608); (6) W. Wagner, (2011), Systemic Liquidation Risk and the Diversity–Diversification Trade-off, Journal of Finance, Vol. 66, Issue 4, pp. 1141–1175; (7) Ph. Strahan, (2013), Too Big To Fail: Causes, Consequences, and Policy Responses, Annual Review of Financial Economics. Vol. 5, pp. 43–61; (8) F. Allen and D. Gale, (2007), Understanding Financial Crises, Clarendon Lectures in Finance, Oxford University Press, Oxford, UK; (9) G. Galati and R. Moessner, (2011), Macroprudential Policy – a Literature Review, BIS Working Papers Nr. 337, Bank for International Settlements, Basel; (10) K. Ueda and B. Weder di Mauro, (2012), Quantifying the Value of the Subsidy for Systemically Important Financial Institutions, IMF Working Paper Nr. WP/12/128; (11) IMF, (2014), How Big Is the Implicit Subsidy for Banks Seen as Too-Important-to-Fail, Global Financial Stability Report, Chapter 3, World Economic and Financial Surveys, April; (12) M. Drehmann and N. Tarashev, (2011), Measuring the Systemic Importance of Interconnected Banks, BIS Working Papers Nr. 342, Bank for International Settlements, Basel; and (13) L. Laeven, et al., (2014), Bank Size and Systemic Risk, Staff Discussion Notes Nr. WP/14/4, IMF, Washington.

For a literature review, see Claessens, (2014), ibid., pp. 11–12.

Claessens, (2014), ibid., p. 8.

For a review regarding these questions, see Claessens, (2014), ibid., pp. 9–10.

See M. Keen and Ruud De Mooij, (2012), Debt, Taxes and Banks, IMF Working Paper WP/12/48, and P. Van den Noord, (2005), Tax Incentives and House Price Volatility in the Euro Area: Theory and Evidence, Economie Internationale, Vol. 101, pp. 29–45.

J. Osiński, et al., (2013), Macroprudential and Microprudential Policies: Towards Cohabitation, IMF Staff Discussion Note 13/05, and P. Angelini, (2012), Macroprudential, Microprudential and Monetary Policies: Conflicts, Complementarities and Trade-offs, Occasional Papers Nr. 140, Bank of Italy.

See S. Claessens et al., (2013), Macro-Prudential Policies to Mitigate Financial System Vulnerabilities, Journal of International Money and Finance, Vol. 39, pp. 153–185. Other models exist; see, for example, European Systemic Risk Board, Heads of Research, (2014), Report on the Macro-Prudential Research Network (MARS), June, or Committee on the Global Financial System (CGFS), (2010), Macroprudential Instruments and Frameworks: A Stocktaking of Issues and Experiences, CGFS Papers Nr. 38, Bank for International Settlements, Basel.

Claessens, (2014), ibid., p. 13. For model development and experiences, see pp. 14–21.

Claessens, (2014), ibid., p. 14.

See in detail: V.V. Acharya, (2013), Adapting Microprudential Regulation for Emerging Markets, in O. Canuto and Swati R. Ghosh (eds.), Dealing with the Challenges of Macro Financial Linkages in Emerging Markets, World Bank, Washington, DC, pp. 57–89, and H.S. Shin, (2013), Adapting Macroprudential Approaches to Emerging and Developing Economies, in Otaviano Canuto and Swati R. Ghosh (eds.), Dealing with the Challenges of Macro Financial Linkages in Emerging Markets, World Bank, Washington, DC, pp. 17–55.

See for details: IMF, (2014), ibid., pp. 75, 79–81.

See for an analysis of that line of thought: B.H. Mandel et al., (2012), The Role of Bank Credit Enhancements in Securitization, Federal Reserve Bank of New York Economic Policy Review, Vol. 34, Issue 2, pp. 225–254.

See in extenso: IMF, (2014), ibid., pp. 76–78.

See in detail: E. Kirby and S. Worner, (2014), Crowd-Funding: An Infant Industry Growing Fast, IOSCO Staff Working Paper 3, International Organization of Securities Commissions, Madrid.

See D. McCrum, (2014), Jumping on the Peer-to-Peer Gravy Train, FT Alphaville (blog) Financial Times, May 21, and Standard & Poor’s (S&P), (2014), As Peer-to-Peer Lending Draws Wider Interest, Does Securitization Lie in Its Future. Standard & Poor’s Financial Services LLC, McGraw Hill Financial, New York, May 2.

See in detail: Kroll Bond Rating Agency (Kroll), 2014, Overview of Mortgage Servicing Rights, New York, Kroll Bond Rating Agency. They indicate further that ‘these rights carry significant short-term risks in terms of compliance and operational factors (such as interruption of servicing or delays in transfers)’; op. cit. IMF, (2014), ibid., p. 77.

See C. Whittall, (2014), Banks Eye SPVs to Ease Capital Pain, International Financing Review, April 29.

This is a longer-term trend. The Chinese government tries to slow that growth pattern down. Therefore, these trends need to be assessed by the reader in their contemporary context, which is a shifting paradigm. Therefore, it was decided to only refer to numbers and percentages when they illustrate somewhat of a longer-term trend and to avoid a situation in which larger parts of the book would be idle by the time it hits the bookshelves. See also Sect. 5.10.

IMF, (2014), ibid., p. 77.

For the data set and the relative contribution of each of the aspects, see IMF, (2014), ibid., p. 79 (Figure 2.7 and Table 2.2).

See in detail: IMF, (2014), ibid., p. 80.

See in detail: IMF, (2014), ibid., pp. 80–81.

See in detail: L. Allen, (2004), The Basel Capital Accords and International Mortgage Markets: A Survey of the Literature, Financial Markets, Institutions and Instruments, Vol. 13, Issue 2, pp. 41–108.

C.W. Calomiris, (2013), The Political Foundations of Scarce and Unstable Credit, Presented at the Federal Reserve Bank of Atlanta Financial Markets Conference, Maintaining Financial Stability: Holding a Tiger by the Tail, Stone Mountain, Georgia, April 9.

Z. Pozsar, (2011), Institutional Cash Pools and the Triffin Dilemma of the U.S. Banking System, IMF Working Paper WP/11/190, International Monetary Fund, Washington.

IMF, (2014), ibid., p. 81.

Especially now that the real estate market is weak and not directly an investment destination for savings as it was in the past for many households.

L. Jianxing and P. Sweeney, (2015), China Issues Draft Rules Restricting Entrusted Loans, Reuters.​com, January 18.

L. Jianxing and P. Sweeney, (2015), ibid.

IMF, (2014), ibid., p. 81.

IMF, (2014), ibid., p. 82.

IMF, (2014), ibid., p. 83 (Figure 2.10).

They indicate that, for example, euro area MMFs seem to be more directly connected with banks and have longer-maturity and less liquid assets than their US and Japanese counterparts (p. 82). For a detailed analysis of their findings, see pp. 82–84. Interesting about the study is that it, as the first according to my knowledge, breaks the aforementioned risks down per category (MMFs, securitization, broker/dealers, finance companies, REITs, funds (of a variety of types)).

IMF, (2014), ibid., pp. 84–86.

See for the methodology in this matter and the review of their findings in the eurozone, the US and the UK: M. Segoviano, et al., (2016), Systemic Risk and Interconnectedness Measures across the Banking and Non-bank Financial Sectors: A Comprehensive Approach, IMF Working Paper, International Monetary Fund, Washington, retaken in 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.

The MCSR does not measure causality and is therefore of a nondirectional nature. The MCSR methodology is based on the methodology proposed by Tarashev et al.; see in detail: N. Tarashev et al., (2010), Attributing Systemic Risk to Individual Institutions, BIS Working Paper Nr. 308, Bank for International Settlements, Basel.

IMF, (2014), ibid., p. 84.

The insurance industry is no longer traditional: it now offers products with non-diversifiable risk, is more prone to a run, insures against economy-wide events and has expanded its role in financial markets. See in detail: V.V. Acharya and M. Richardson, (2014), Is the Insurance Industry Systemically Risky? In Modernizing Insurance Regulation, (eds.) by J.H. Bigg and M.P. Richardson, John Wiley & Sons, Hoboken. See also: A. Jobst, (2014), Systemic Risk in the Insurance Sector, The Geneva Papers on Risk and Insurance. Issues and Practices; M. Billio et al., (2010), Econometric Measures of Systemic Risk in the Finance and Insurance Sectors, NBER Working papers, Nr. 16,223; The Geneva Association, (2010), Systemic Risk in Insurance. An Analysis of Insurance and Financial Stability. Special Report of The Geneva Association Systemic Risk Working Group; R. S.J. Koijen and M. Yogo, (2013), Shadow Insurance, NBER Working Paper Nr. 19,568; 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;

Discussed in the shadow insurance subsegment elsewhere in the book where I spent more time on the material issues regarding shadow insurance.

See the annual FSB shadow banking monitoring reports, which document those interlinkages extensively.

See for a few in detail: J.V. Duca, (2014), What Drives the Shadow Banking System in the Short and the Long Run, Federal Reserve Bank of Dallas, Working Paper Nr. 1401, pp. 2 ff. As often, one can discuss what caused what exactly.

Duca, ibid., p. 24.

See for further details:

‘In the short-run, the shadow bank-funded share not only fell when short-run liquidity premia were high, term premia reflected expectations of an improving economy, or event risks occurred in security markets, but also rose when deposit rate ceilings were more binding or short-run regulatory changes favored nonbank relative to bank finance’; Duca, ibid., p. 25.

P.E. McCabe, et al., (2013), Minimum Balance of 5 Percent Could Prevent Future Money Market Fund Runs, Brookings Papers on Economic Activity, Vol. Spring 2013, pp. 211–278; E.S. Rosengren, (2014), Our Financial Structures—Are They Prepared for Financial Stability? Conference on Post-Crisis Banking Amsterdam, the Netherlands, June 29, 2012; Journal of Money, Credit, and Banking, 2014, Vol. 12, pp. 395 ff.

Duca, (2014), ibid., p. 26.

This is in line with a long list of historical literature. See Duca, (2014), p. 24.

C. Goodhart, (2008), The Boundary Problem in Financial Regulation, National Institute Economic Review, Vol. 206, Issue 1, pp. 48–55, and C. Goodhart and R. M. Lastra, (2010), Border Problems, Journal of International Economic Law, Vol. 13, Issue 3, pp. 705–718.

IMF, (2014), ibid., p. 87.

IMF, (2014), ibid., p. 87.

For the extensive write-up of IMF, see (2014), ibid., pp. 87–89.

Which is discussed further in this chapter.

E.F. Greene and E. L. Broomfield, (2015), Dividing (and Conquering?) Shadows: FSB and U.S. Approaches to Shadow Banking at the Dawn of 2014, Included in Shadow Banking Within and Across National Borders (eds. S. Claessens, D. Evanoff, G. Kaufman, and L. Laeven, 2015), World Scientific Studies in International Economics: Volume 40.

See: (1) S.L. Schwarcz, (2014), The Functional Regulation of Finance, Unpublished manuscript, Duke University (regarding regulating a dynamic financial environment) and (2) P. Tucker, (2014), Financial Regulation Needs Principles as well as Rules, Financial Times, June 19. Other publications by Schwarcz on the matter include S.L. Schwarcz, (2016), Regulating Financial Chance: A Functional Approach, Vol. 100, Minnesota Law Review, pp. 485–496; and S.L. Schwarcz, (2012), Controlling Financial Chaos. The Power and Limits of Law, Wisconsin Law Review, Vol. 3, pp. 815–840.

See, for example, FSB, (2013), Global Shadow Banking Monitoring Report 2013, Annex 2, UK Resident Banks’ Repo Books: Mapping and Illustrative Risks, pp. 32–36.

P. Mehrling, et al. (2012), Bagehot was a Shadow Banker: Shadow Banking, Central Banking, and the Future of Global Finance, Working paper, mimeo.

They are also publicly available through fsb.​org.

See FSB, (2013), Global Shadow Banking Monitoring Report 2013, pp. 10–13.

FSB, (2013), Global Shadow Banking Monitoring Report 2013, November 14, Basel, p. 3.

USD 352 trillion and USD 52 trillion, respectively, for 2018, see the FSB 2019 nonbank financial intermediation report, February 4, 2019, via fb.​org. With respect to the FSB annual shadow banking monitoring reports, please note that year of publication, year of report and year of data tend to be different: for example, the 2019-issued document is the 2018 report covering 2017 data.

See in detail FSB, (2013), ibid., pp. 13–17. FSB, (2014), ibid., pp. 14. For the update data for 2013–2014, see infra this chapter under ‘dynamics of the contemporary global shadow banking market’.

FSB, (2013), Global Shadow Banking Monitoring Report, Basel, p. 15, and FSB, (2014), Global Shadow Banking Monitoring Report, Basel, p. 14; FSB, (2014), ibid., pp. 6–7, 13–14; FSB, (2015), ibid., pp. 39–40.

FSB, (2013), ibid., p. 17.

FSB, (2013), ibid., p. 19.

FSB, (2013), ibid., p. 21.

This methodology is based on aggregate balance sheet exposure (assets and liabilities of banks to OFIs) between the two sectors. See FSB, (2013), ibid., p. 22, for a visualization of the protocol. The FSB indicates that data constraints restrict their ability to refine these measures further to distinguish, for instance, the interconnectedness between banks and different types of shadow banking entities. This remains an important gap. Different shadow banking entities are associated with different risk factors such as credit intermediation, maturity transformation, and leverage.

FSB, (2013), ibid., p. 40.

FSB, (2013), ibid., pp. 40–41.

Contrary to securitization, a loan fund is not tranched into slices of different seniority. In addition, many loan funds can have long reinvestment periods, and potentially are infinitely long-lived, while securitization vehicles have a finite live.

FSB, (2013), ibid., p. 43.

Also in Europe. See recently: Communication from the Commission to the European Parliament and the Council on Long-Term Financing of the European Economy. Com (2014), 168 final (March 27, 2014).

FSB, (2013), ibid., pp. 43–44.

Z. Pozsar, et al., (2010), Shadow Banking, Federal Reserve Bank of New York Staff Reports, nr. 458, NY.

J. Li et al., (2013), Shadow Banking Systems in the US and China, Working Paper p. 1; J. Li, et al. (2014), Shadow Banking in China: Institutional Risks (in Chinese). China Economic Review, Vol. 31, pp. 119–129; G. Lan, (2015), Insights from China for the United States: Shadow Banking, Economic Development, and Financial Systems, Berkeley Business Law Journal, Vol. 12, Issue 1, pp. 144–195; D. Luo, (2016), Shadow Banking and Its Development in China, The Development of the Chinese Financial System and Reform of Chinese Commercial Banks, The Nottingham China Policy Institute Series. Palgrave Macmillan, London, pp. 181–201.

G. Gorton and A. Metrick, (2010), Regulating the Shadow Banking System, Brookings Papers on Economic Activity 2010, pp. 261–312.

E.J. Kane, (2012), The Inevitability of Shadowy Banking, Paper presented at the Federal Reserve Bank of Atlanta, 2012 Financial Markets Conference, April 10.

J. Li et al., (2013), ibid., p. 2.

J. Li, (2010), The Change in the Scale of Unobserved Loan in China from 1978 to 2008, Journal of Financial Research, April 2010, Nr. 358, pp. 40–50.

See also for a recent overview and update: T.V. Dang, (2019), Shadow Banking Modes: The Chinese versus US System, Columbia Working Paper, October.

Leverage ratios between 2000 and 2009 for banks worldwide were at about a mean of 12.39, while investment banks had leverage ratios of a mean of 13.269, large noninvestment banks had leverage ratios of about 17.379, sponsor banks (banks that had off-balance sheet investment vehicles, mainly large commercial banks) had leverage ratios of about 22.712, and nonsponsor banks had leverage ratios of about 12.375 (see: S. Kalemli-Ozcan, et al., [2011], Leverage Across Firms, Banks and Countries. NBER Working Paper Nr. 17,354).

Rehypothecation is the process through which brokers post their clients’ collateral to obtain additional funds or assets.

Li, et al. (2013), ibid., p. 4.

Li et al., (2013), ibid., p. 7. Multiple examples can be found at Li et al. (2013), ibid., pp. 6–9.

For an overview of regulatory measures in both countries, see Li et al., (2013), ibid., pp. 10–12.

E.F. Greene and E.L. Broomfield, (2013), Dividing (and Conquering?) Shadows: FSB and US Approaches to Shadow Banking at the Dawn of 2014, Working Paper, p. 10.

M. Thiemann, (2013), In the Shadow of Basel. How Competitive Politics Bred the Crisis, Foundation for European Progressive Studies, Center for Capitalism, Globalization and Governance, Working Paper, January. He focuses also on the asymmetry in the regulatory and accounting coverage of the shadow banking sector and securitization in particular.

FSB, (2014), Global Shadow Banking Monitoring Report 2014, p. 1.

FSB, (2014), Global Shadow Banking Monitoring Report 2014, p. 2.

FSB, (2013), Strengthening Oversight and Regulation of Shadow Banking Policy Framework for Strengthening Oversight and Regulation of Shadow Banking Entities, August 29.

See, for example, regarding Switzerland the highly recommended country analysis in Appendix 2 of FSB, (2014), Global Shadow Banking Monitoring Report 2014, pp. 30–35.

FSB, (2015), Global Shadow Banking Monitoring Report 2015, pp. 1–2.

See in detail FSB, (2015), Global Shadow Banking Monitoring Report 2015, pp. 6–19.

See in detail: FSB, (2015), ibid., pp. 17–19.

See also for a write-up: FSB, (2015), ibid., pp. 20–22.

See for their analysis on the matter: FSB, (2015), ibid., pp. 22–25.

FSB, (2015), ibid., pp. 11–14.

FSB, (2015), ibid., pp. 27–33.

See in detail: FSB, (2014), Global Shadow Banking Monitoring Report 2014, pp. 2–4. FSB, (2015), Global Shadow Banking Monitoring Report 2015, pp. 1–5.

FSB, (2014), Global Shadow Banking Monitoring Report 2014, pp. 5–6.

This was defined in their 2011 reporting. See in detail: FSB, (2011), Shadow Banking: Scoping the Issues, pp. 2–6.

FSB, (2014), Global Shadow Banking Monitoring Report 2014, pp. 6–7.

FSB, (2014), Global Shadow Banking Monitoring Report 2014, p. 7.

FSB, (2014), Global Shadow Banking Monitoring Report 2014, p. 10.

See in detail FSB, (2014), Global Shadow Banking Monitoring Report 2014, pp. 11–13.

FSB, (2014), Global Shadow Banking Monitoring Report 2014, p. 13.

The FSB provides the following clarification in this matter: ‘[f]or instance, a corporate bond issued to investors is not considered part of a credit intermediation chain as it forms a direct bilateral link. A corporate bond that is owned through a mutual fund on the other hand is a form of credit intermediation and would be accounted for as part of the assets under management of the investment fund’ (p. 19, footnote 38).

FSB, (2014), Global Shadow Banking Monitoring Report 2014, p. 19.

See for a detailed review: FSB, (2014), Global Shadow Banking Monitoring Report 2014, pp. 19–24.

The FSB specifies those caveats as follows: ‘[f]irst, some of the assets related to the self-securitisation might at some point be sold to third parties when financial conditions improve. Second, the “pure” Equity Investment Funds might also engage indirectly in some credit intermediation activities, for example if they lend securities against cash collateral to gain additional revenues. Third, some equity REITs exhibit risk characteristics of shadow banking such as vulnerability to runs, liquidity mis-matches, leverage, and maturity transformation, which suggests that monitoring is warranted regardless of their formal classification. Fourth, a number of jurisdictions do not provide granular enough data to allow the narrowing down’; FSB, (2014), ibid., pp. 23–24.

FSB, (2014), Global Shadow Banking Monitoring Report 2014, pp. 24–27.

FSB, (2014), Global Shadow Banking Monitoring Report 2014, p. 24.

The interpretability of the results of these ratios should be treated with caution. As a change in both nominator and denominator can impact the result, thus the interpretation can materially deviate, although the end result will be similar. ‘For example, a comparable increase in credit risk for banks may in one case be driven by a surge in banks’ assets to OFIs, while in another case by a decrease in bank assets. In that case, an increase in this ratio should not be interpreted in the same way in both cases’ (p. 26, footnote 51).

In Spain, this was mainly due to a large decrease of bank assets in 2013. This also works through in the later-to-be-discussed ‘funding risk for banks’. The increase in banks’ assets to OFIs as a share of OFI assets was partly driven by a fall in OFI assets in 2013 (p. 26, footnote 52).

FSB, (2014), Global Shadow Banking Monitoring Report 2014, p. 27.

FSB, (2014), Global Shadow Banking Monitoring Report 2014, pp. 27 and 52–55.

FSB, (2015), Global Shadow Banking Monitoring Report 2015, pp. 26–33.

FSB, (2015), Global Shadow Banking Monitoring Report 2015, pp. 33–44.

See for a breakdown per country of total financial assets: FSB, (2015), ibid. Annex 4, pp. 57–59.

M Brei, (2013), Offshore Financial Centers in the Caribbean: An Overview, Université Paris Ouest – Nanterre, Working Paper, mimeo.

M. Thiemann, (2012), Out of the Shadows? Accounting for Special Purpose Entities in European Banking Systems, Diss., unpublished; see also: M. Thiemann, (2012), Out of the Shadows?’ Accounting for Special Purpose Entities in European Banking Systems, Competition and Change, Vol. 16 Nr. 1, February, pp. 37–55.

S. Naitram, (2014), Offshore Financial Centers in the Global Capital Network, Global Economy Journal, Vol. 14, Nr. 3, pp. 435–451.

For example, A. K. Rose and M. M. Spiegel, (2005), Offshore Financial Centers: Parasites or Symbionts? FRB of San Francisco Working Paper Nr. 2005–05; A. Nesvetailova, (2014), Shadow Banking and the Political Economy of Financial Innovation, Working Paper, mimeo; T. Rixen, (2016), Offshore Financial Centres, Shadow Banking and Jurisdictional Competition: Incrementalism and Feeble Re-regulation, Working Paper, mimeo;

A.P. Morriss, (2008), The Role of Offshore Financial Centers in Regulatory Competition, University of Illinois Law & Economics Research Paper Nr. LE07–032.

C.M. Boise, (2008), Regulating Tax Competition in Offshore Financial Centers, Case Legal Studies Research Paper Nr. 08–26.

R. Fernandez, (2014), Shadow Banking and European Offshore Financial Centers: The Blind Spot in EU Policy debates, KU Leuven Working Paper, mimeo.

R. Palan and A. Nesvetailova, (2014), Elsewhere, Ideally Nowhere: Shadow Banking and Offshore Finance, Cityperc Working Paper, Nr. 2014/1, p. 1, published in Politik (2014), Vol. 16, Issue 4, pp. 26–34.

T. Veblen, (1904), The Theory of Business Enterprise, Augustus M Kelley, Clifton, NY, and T. Veblen, (1923), Absentee Ownership and Business Enterprise in Recent Times: The Case of America, Beacon Press, Boston.

Veblen, (1923), ibid., p. 278. See also more recently J. Urry, (2014), Offshoring, Polity, London. He documents the various patterns of offshoring of the economy, sociability, politics and the environment. In every scenario, offshoring generates new patterns of power, thereby limiting the conditions for democratic governance.

Palan and Nesvetailova, (2014), ibid., p. 3.

Palan and Nesvetailova, (2014), ibid., p. 4.

P. Mehrling, (2012), Shadow Banking, Central Banking and the Future of Global Finance, Working Paper.

A concept first coined by John Commons which in short refers to the inclination of the law and economics sciences (in contrast to natural sciences) to refer to value(s) in future times and when quantifying try to escape the natural laws of gravity, that is, reality. See for that in detail: J. R. Commons, (2002), Selected Essay, Volume I, M. Rutherford and W.J. Samuels (eds.), Routledge, London, pp. 337 ff.

A. Nesvetailova, (2014), A Crisis of the Overcrowded Future: Shadow Banking and the Political Economy of Financial Innovation, New Political Economy, September 26. See also P. Lysandrou, and A. Nesvetailova, (2014), The Role of Shadow Banking Entities in the Financial Crisis: A Disaggregated View, Review of International Political Economy.

See also A. Nesvetailova and R. Palan, (2013), Sabotage in the Financial System: Lessens from Veblen, Business Horizons, Online first.

A. Haldane, (2012), On Being the Right Size, The Beesley Lectures, October 25, speech given at the Institute of Economic Affairs, via bis.​org

That traditional economic understanding might have led the Dutch regulator to develop the idea of creating a size limit to banks and the banking sector; see DNB, (2015), Annual Report 2014, De Nederlandse Bank, Amsterdam.

A. Nesvetailova and R. Palan, (2013), ibid., pp. 10–11. See also P. Langley, (2015), Liquidity Lost: The Governance of the Global Financial Crisis, Oxford University Press, Oxford.

Many studies have been done, and depending on the way they measure that share, it equates somewhere between USD 21 and 60 trillion, and so on both counts this is a very sizeable part of the global economy. See, for example, J. Henri, (2012), The Price of Offshore Revisited: A Review of Methods and Estimates for ‘Missing’ Global Private Wealth, Income, Inequality, and Lost Taxes, Tax Avoidance, Corruption and Crisis, University of Essex, July 5–6, 2012.

R. Palan, (2013), The Financial Crisis and Intangible Value, Capital & Class, Volume February.

R. Palan and A. Anastasia, (2013), The Governance of the Black Holes of the World Economy: Shadow Banking and Offshore Finance, City University London (CITYPERC) Working Paper Series Nr. 2013/3, p. 6.

Some see the terms ‘tax haven’ and ‘offshore financial centers’ as interchangeable, although they are not exactly identical as they can serve different purposes. Most locations are both however.

R.C. Bryant, (1983), Eurocurrency Banking: Alarmist Concerns and Genuine Issues, OECD Economic Studies Nr. 1.

R. Palan and A. Anastasia, (2013), ibid., pp. 8–11.

F. Schneider, (2010), Shadow Economies All over the World. New Estimates for 162 Countries from 1999 to 2007, WPS5356, July, the World Bank Development Research Group Poverty and Inequality Team & Europe and Central Asia Region Human Development Economics Unit.

J. Sharman, (2006), Havens in a Storm: The Struggle for Global Tax Regulation, Cornell University Press, Ithaca.

Z. Pozsar et al., (2010), The Shadow Banking System. Staff Report Nr. 458, July, Federal Reserve of New York, p. 29. Also: M. Amato, and L. Fantacci, (2012), The End of Finance, Polity Press Oxford.

A. Buehn, and F. Schneider, (2012), Shadow Economies Around the World: Novel Insights, Accepted Knowledge, and New Estimates, International Tax and Public Finance, Vol. 19, pp. 139–171.

R. Palan and A. Anastasia, (2013), ibid., p. 14.

B. Michael, (2014), Playing the Shadowy World of Emerging market Shadow Banking, Institute for Emerging Market Studies (IEMS), Moscow School of Management Skolkovo, IEMS Emerging Market Brief, Vol. 14–02, Moscow, April.

See, for example, B. Michael, (2014), ibid., p. 7, and the FSB’s Annual Global Shadow banking Monitoring Reports.

B. Michael, (2014), ibid., pp. 9–11.

See for an overview of the performances of shadow banking assets versus traditional lending products: B. Michael, (2014), ibid., pp. 14–17.

R. Lavigne et al., (2014), Spillover Effects of Quantitative Easing on Emerging-Market Economies, Bank of Canada Review, autumn, pp. 23–33; Dongchul Cho and Changyong Rhee, (2013), Effects of Quantitative Easing on Asia: Capital Flows and Financial Markets, Asian Development Bank, Working Paper Nr. 350, June.

P. Mishra et al., (2014), Impact of Fed Tapering Announcements on Emerging Markets, IMF Working Paper Nr. WP/14/109; T. Bouraoui, (2015), The Effect of Reducing Quantitative Easing on Emerging Markets, Applied Economics, Vol. 47, Issue 15, pp. 1562–1573.

B. Michael, (2014), ibid., p. 17.

For an overview of the different product groups and their assessment in terms of why ‘synthetic’ lending offers far higher rates of return than plain shadow banking instruments, see B. Michael, (2014), ibid., p. 18. He also sees a prosperous future for SB entities in the larger emerging economies if they are ready to stall the implementation of FSB reforms; see for his assessment, pp. 22–27.

See the IMF, (2012), Global Financial Stability Annual Report, Basel, for more details on the progress of individual emerging economies.

See for an evaluation of the FSB recommendations and their potential impact on shadow banking activities; B. Michael, (2014), ibid., pp. 30–31.

Content-wise this chapter is fully and exclusively dependent on the 2016, 2017 and 2018 Global Shadow Banking reports as released annually by the FSB. See FSB, (2017), Global Shadow Banking Monitoring Report 2016, May 10; FSB, (2018), Global Shadow Banking Monitoring Report 2017, March 5, and FSB, (2019), Global Monitoring Report on Non-Bank Financial Intermediation 2018, February 4. Observe that this is the first year that the FSB uses the term ‘non-bank financial intermediation’ to indicate their annual shadow banking report. The term change is official since 22 October 2018. In the country-specific overview in this book, all other regional or domestic sources and literature have been used and reflected.

For the 2015 data (2016 report), they covered 28 jurisdictions representing over 80% of GDP. These became 29 jurisdictions and 80%, respectively, in the 2017 and 2018 report.

See in detail FSB, (2017), Global Shadow Banking Monitoring Report 2016, May 10, pp. 1–5 and the 2017–2018 report issued, respectively, in 2018–2019, all via fsb.​org. Footnote references are made to individual reports.

Monitoring Universe of Nonbank Financial Intermediation, also referred to as nonbank financial intermediation. It refers to all nonbank financial intermediation, including OFIs, insurance companies and pension funds. See for the terminology used, for example, FSB, (2018), Global Shadow Banking Monitoring Report 2017, March 5, pp. 2, Box 0–1. It also includes ‘Financial auxiliaries’ being ‘financial corporations that are principally engaged in activities associated with transactions in financial assets and liabilities or with providing the regulatory context for these transactions but in circumstances that do not involve the auxiliary taking ownership of the financial assets and liabilities being transacted’; see: FSB, (2018), ibid., p. 6, footnote 16.

With China as a notable exception.

FSB, (2018), ibid., pp. 11–12.

FSB, (2018), ibid., pp. 12–14, including exhibit 2.4: OFIs represent the largest share of Luxembourg’s financial system. They have a large segment denominated as CFIMLs. These CFIMLs are largely set up for financial management, asset structuring and fund-raising purposes by multinational firms to channel funds to other parts of their own firm or to attract external funding for their parent company, with very little engagement in any investment or borrowing with entities external to the group. Investment funds are the other large sector in Luxembourg. See in detail: C. Duclos and R. Mohrs, (2017), Analysis of the Shadow Banking Content of Captive Financial Companies in Luxembourg, Comité du Risque Systémique Report, April.

Other financial intermediaries. It comprises all financial institutions that are not classified as banks, insurance corporations, pension funds, public financial institutions, central banks or financial auxiliaries. It can be seen as a proxy for a broad measurement of shadow banking.

See regarding trust companies in China: FSB, (2018), ibid., pp. 26, box 2–5 and the extensive coverage in the China chapter in this book.

The MMF market is highly concentrated, and nearly half the global assets are located in five countries (the US, China, Ireland, France and Luxembourg); FSB, (2018), ibid., p. 20.

Account for a small portion of OFIs (3.8% in 2016); for actual data (most recent 2019), see the International Organization of Securities Commissions’ (IOSCO’s) recurring ‘hedge fund survey (via iosco.​org). The Cayman Islands is still the preferred location accounting for 87% (2016) of total hedge fund assets. Recent insights reveal that (1) leverage is on the rise (aggregate of values of long and short positions), divided by the net asset value or NAV (net leverage is defined as the value of long positions minus the value of short positions, divided by the NAV, and is a more accurate measure for leverage), (2) borrowing is on the rise with prime broker as their preferred source, (3) liquidity seems warranted under normal market conditions. There is concern about the European dynamic to redress hedge funds as UCITS regulated mutual funds as this way they can also be a market toward non-qualifying/retail investors.

See for a historical breakdown of the categories included in the OFI segment: FSB, (2018), ibid., p. 19 Exhibit 2.9

Consider them in-house banks within nonfinancial corporate groups. They come in three models: (1) they facilitate channeling of funds; (2) they raise funding in the marketplace and lend onward to group companies; and (3) they perform treasury management services to the group. See in detail: FSB, (2018), ibid., pp. 25–26. Luxembourg, the Netherlands and, recently (after requalification), also China are preferred destinations.

FSB, (2018), ibid., pp. 15–16.

See in detail FSB, (2018), ibid., pp. 16–18.

FSB, (2018), ibid., pp. 26–28.

FSB, (2018), ibid., pp. 28–30.

That is, wholesale funding or repos as a percentage of total balance sheet assets.

FSB, (2018), ibid., pp. 30–33. Wholesale funding and repos have the potential outside the banking scene to create short-term, money-like liabilities, engage in credit growth and conduct maturity and liquidity transformation. CGFS, (2017), Repo Market Functioning, April. OFIs continue to be net providers of cash to the financial system from repos. MMFs contribute as they provide cash through repos. The repo market, on aggregate still grows, but has structurally changed. Banks appear less willing to intermediate and the share of broker-dealers differs materially across jurisdictions.

See for a stylized mapping of possible relations: FSB, (2018), ibid., pp. 35–36, box 3.1: the interconnectedness in the financial system varies quite substantially across jurisdictions. Insurance and pension funds tend to be less connected in Asia and Europe but more in the Americas, where their exposure is higher than that of banks.

FSB, (2018), ibid., p. 36.

See FSB, (2017), Global Shadow Banking Monitoring Report 2016, May 10, pp. 32–37 and FSB, (2018), Global Shadow Banking Monitoring Report 2017, March 5, pp. 36–40 for an analysis both ways, that is, bank credit risk to OFIs, bank funding risk from OFIs, OFI credit risk to bank and OFI funding risk from banks. Other forms of interconnectedness identified are (1) the interconnectedness of insurance companies and pension funds to OFIs (insurance companies and pension funds hardly rely on OFIs for funding); the other way around in a more elevated form in certain jurisdictions, for example, Belgium, Brazil (FSB, [2018], ibid., pp. 41–43, box 3–3), the Netherlands, see pp. 36–39 (2017) and pp. 40–43 (2018); (2) cross-border interconnectedness: the data suggests a wide range of financial interconnectedness between similar types of domestic financial intermediaries and foreign intermediaries or investors (see p. 39, 2017).

See FSB, (2017), Global Shadow Banking Monitoring Report 2016, May 10, pp. 29–41, and in particular Box 3.1 pp. 30–31. The granular breakdown suggests that (1) banks and OFIs remain the most interconnected, (2) the interconnectedness of both pension funds and insurance corporations to OFIs and to banks is also material, (3) the variance across jurisdictions is large. Some questions remain unanswered: for example, does the actual funding and credit risk have the potential to affect stability risk through excessive leverage or maturity/liquidity mismatches? Further questions involve the impact of the concentration of these exposures and how the interconnectedness have a cross-border element that can lead to international spillovers. See regarding the latter: S. Tungson et al., (2017), Relation Between Regional Uncertainty spillovers in the Global Banking System, Working Paper, February 20, mimeo.

Traditionally, two measures of risk from interconnectedness can be measured, and this in line with P. Glasserman and H.P. Young, (2015), How Likely Is Contagion in Financial Networks, Working Paper, mimeo, and A. Cabrales et al., (2017), Risk-Sharing and Contagion in Networks, Working Paper, April 26, mimeo. The first measurement is that of borrowing centrality, given by the right eigenvector of the ‘whom-to-whom’ matrix, which provides information about potential default cascades. A shock to a specific entity type, which would put them close to insolvency, would result in direct losses to other entities in that same row (e.g. OFIs). Further, additional losses could be incurred through the propagation of these defaults, in case, for example, bank would start defaulting on their own creditors. The measurement proposed captures therefore the relative contribution of each type to both direct and indirect losses. The second way of measuring interconnectivity risk is through measuring funding centrality, which is defined by the left eigenvector of the ‘whom-to-whom’ matrix. It captures the importance of an entity to provide funding. In case an adverse shock forces a certain subcategory to reduce funding to other subcategories, the direct versus indirect criteria becomes relevant. The importance of OFIs as funding providers is relevant not only through their direct exposures, but also on the funding exposures of their counterparties, which are often bank. Banks continue to be the central node. They are the main source of funding for most financial entities. OFIs are from a funding point of view relatively more important relative to the size their position in the research suggests. Both measures are key ‘because they quantify how interconnected an entity is relative to other entities and take into consideration how central the connecting entities are’. See in detail FSB, (2017), Global Shadow Banking Monitoring Report 2016, May 10, pp. 30–31, Exhibit 3.10: the new and third measurement might not yield large differences in quantitative analysis relative to the initial two ways of measuring, but observed differences might be economically meaningful. For example, the role of OFIs as a source of funding and credit risk was historically underreported. The borrowing and funding centrality is therefore key in accurately measuring the risk stemming from interconnectedness. I concluded in a similar way earlier on, when designing a Pigovian model of taxation for shadow banking entities based on Markose’s models (see the Pigovian chapter in Vol. II of this reference work for an updated version of this line of thought; initially to be found L. Nijs, (2015), Neoliberalism 2.0. Organizing and Regulating Globalizing Markets, Palgrave, chapter 6). As time progressed, these measures of centrality have been consistently found to be superior in mapping systemic risk, in particular as they are immune to the gross size of the financial system, which can be considered as a separate source of risk (see most recently A. Alter et al., [2015], Centrality-based Capital Allocations, Federal Reserve Bank of Cleveland, Federal Reserve Bank of Cleveland, Working Paper Nr. 15–01, January 29). Still, there are some open exposures in the measurement model, that is, the risks from indirect interconnectedness and some qualitative aspects, for example, the difference between secured and unsecured liabilities.

The interconnectedness can be mostly explained by investors’ search-for-yield behavior, financial linkages between banks, capital stringency and demand from institutional investors: T. Fong et al., (2018), Assessing the Interconnectedness Between Cross-Border Shadow Banking Systems, Hong Kong Institute for Monetary Research Working Paper Nr. 5/2018; V. Bruno and H.S. Shin, (2015), Macroeconomic Determinants of Cross-Border Banking and Global Liquidity, Review of Economic Studies, Vol. 82, pp. 535–564.

J. Abad, et al., (2017), Mapping the Interconnectedness Between EU Banks and Shadow Banking Entities, ESRB Working Paper Nr. 40, March; V. Bruno and H.S. Shin, (2015), Cross-Border Banking and Global Liquidity, Review of Economic Studies, Vol. 82, pp. 535–564. The complexity and size of a shadow banking system tends to be directly correlated with the size and complexity of the real economy (albeit there are exceptions, e.g. the Netherlands, Ireland, Luxembourg, IFCs offshore): T. Barbu et al., (2016), Shadow Banking – Evidence from EU countries, Review of Economic & Business Studies, Vol. 9, Issue 2, pp. 111–129; J.V. Duca, (2016), How Capital Regulation and Other Factors Drive the Role of Shadow Banking in Funding Short-Term Business Credit, Journal of Banking & Finance, Vol. 69, pp. 10–24; P. Lysandrou and A. Nesvetailova, (2015), The Role of Shadow Banking Entities in the Financial Crisis: A Disaggregated View, Review of International Political Economy, Vol. 22, Issue 2, pp. 257–279.

FSB, (2018), ibid., pp. 14–15.

Refers to the narrow measure of shadow banking under the ‘economic functions approach’. It includes nonbank financial entity types that are considered by authorities to be involved in credit intermediation where financial stability risks from shadow banking may occur, based on the FSB’s methodology. See FSB, (2017), Global Shadow Banking Monitoring Review 2016, May 10, pp. 42–50; FSB, (2018), Global Shadow Banking Monitoring Review 2017, March 5, pp. 45 ff. Excluded are pension funds, insurance corporations, financial auxiliaries and OFIs not involved in any of the five economic functions (EFs) and entities consolidated in banking groups. See for a full write-up of the narrowing down process FSB, (2018), ibid., p. 73–74, Annex 2.

The US had the largest shadow banking sector (40% of global total), followed by the Cayman Islands, Japan and Ireland. The number of jurisdictions included in the analysis grows annually (2015: 27, 2016: 29).

See for further details and observations: FSB, (2017), ibid., pp. 45–48.

FSB, (2013) Policy Framework for Strengthening Oversight and Regulation of Shadow Banking Entities, August. The model however is refined each year; see FSB, (2017), Global Shadow Banking Monitoring Report 2016, May 10, pp. 43–44; FSB, (2019), ibid., p. 7. For a full worked out summary table (both in percentages as well as absolute amounts), see FSB, (2019), ibid. Annex 1, p. 93.

Data based on the annual shadow banking monitoring reports (2016–2019) of the FSB.

Theoretically this difference should be zero but in practice that difference turns out to be quite large in some jurisdiction. In aggregate it amounts USD 5.3 trillion.

And an average of 13% since the measurement started in 2011.

See in detail: FSB, (2017), Global Shadow Banking Monitoring Review 2016, May 10, pp. 13–15.

See in detail: FSB, (2017), ibid., pp. 16–24.

See in detail: FSB, (2017), ibid., pp. 25–26.

See in detail: FSB, (2017), ibid., pp. 27–28.

FSB, (2017), ibid., pp. 24–25, Box 2.2.

See FSB, (2017), Global Shadow Banking Monitoring Report 2016, May 10, pp. 54–57, Exhibit 5–5.

FSB, (2017), Global Shadow Banking Monitoring Report 2016, May 10, pp. 51–67; FSB, (2018), Global Shadow Banking Monitoring Report 2017, March 5, pp. 53–70.

Although this was largely solved beginning the 2017 FSB report.

Traditional measures of leverage in the financial system tend to reflect bank balance sheet data. These traditional, bank-centric measures should be augmented by considering pledged collateral in the financial system since pledged collateral provides a measure of an important part of nonbank funding to banks. A broader view on leverage will enhance our understanding of global systemic risk and complement the theoretical work in this field by providing a link from micro-level leverage data to macro aggregates such as credit to the economy. See in detail: M. Singh and Z. Alam, (2018), Leverage, A Broader View, IMF Working Paper Series Nr. WP/18/62, March.

For a good recent review of literature on the matter, see M. Singh and Z. Alam, (2018), ibid., pp. 6–7.

This is mainly a problem for global systematically important banks (G-SIBs), which have a global footprint in niche transactions such as peddling pledged collateral or wealth management products that may not be on the balance sheet. See: T. Adrian, (2017), Shadow banking and Market-Based Finance, Speech prepared for the 33rd SUERF colloquium held at the Bank of Finland in Helsinki, Finland, on 14–15 September 2017.

It is often overlooked but always at the heart of financial vulnerabilities and crises; see in detail: S. 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. Also see D. Aikman et al., (2016), Financial Vulnerabilities, Macroeconomic Dynamics, and Monetary Policy, Finance and Economics Discussion Series Nr. 2016–55, Federal Reserve Board, Washington, DC, August; O. Röhn, et al. (2015), Economic Resilience: A New Set of Vulnerability Indicators for OECD Countries, OECD Economics Department Working Papers, Nr. 1249, OECD Publishing, Paris.

H.S. Shin, (2017), Leverage in the Small and in the Large, Panel Remarks at the IMF conference on Systemic Risk and Macroprudential Stress Testing, Washington, DC, 10 October 2017. Leverage in the small refers to the leverage of individual institutions, while leverage in the large refers to the leverage of the financial system as a whole. Shin refers to two different aspects in gauging systemic risk. The first is to ‘drill down’ and looks in detail how institutions are intertwined in a complex web of interactions. The second is to ‘drill up’ to macro and often global aggregates. Drilling up is often informative as it delivers the all-important time dimension of systemic risk—how it builds up over time and how it unwinds. He argues for two propositions: ‘[f]irst, mitigating complexity is mostly about taming leverage in the small. The motto is: if you take care of leverage in the small, complexity will take care of itself. Second, lest we fall into complacency, taming complexity is not enough to ward off systemic risk. Systemic risk is mostly about leverage in the large. Addressing systemic risk entails taking a macro and global perspective. Here, the motto is: take a global approach to macroprudential frameworks.’

M. Singh and Z. Alam, (2018), Leverage, A Broader View, IMF Working Paper Series Nr. WP/18/62, March. They suggest that ‘[a] key step towards addressing this issue would be to properly account for pledged collateral transactions and other transactions such as wealth management accounts, or other areas that generally fall under the rubric of shadow banking’ (p. 19). Part of that process will also include a harmonization or uniformization of leverage reporting under GAAP and IFRS.

See for details FSB, (2017), Global Shadow Banking Monitoring Report 2016, May 10, Annex 2, pp. 71–72.

FSB, (2019), Global Monitoring Report on Non-bank Financial Intermediation 2018, February 4, pp. 4–7. To avoid confusion, it is the 2018 FSB report on nonbank financial intermediation, published in February 2019, covering data up till the end of 2017. The key terms and definitions used are as follows: MUNFI, also referred to as nonbank financial intermediation, is a broad measure of all NBFI, and includes pretty much everything (insurance companies, pension funds, etc.); OFIs comprise all financial institutions that are not central banks, banks, insurance corporations, pension funds, public financial institutions or financial auxiliaries; narrow measure of nonbank financial intermediation (or ‘narrow measure’) includes nonbank financial entity types that authorities have assessed as being involved in credit intermediation activities that may pose bank-like financial stability risks (p. 7, Box 0–1). Jurisdiction-specific overviews can be found: FSB, (2019), ibid., pp. 93–94.

FSB, (2019), ibid., pp. 15–21.

FSB, (2019), ibid., pp. 13–14.

The narrow measure process calculations can be found: FSB, (2019), ibid., pp. 95–96.

Growing from 61% (2011) to 75% (2017) of GDP; FSB, (2019), ibid., p. 44. The relative size and recent evolution of the narrow measure of NBFI varies substantially across jurisdictions however (pp. 45–46).

For details: FSB, (2019), ibid., pp. 40 ff.

The growth of the narrow measure has been driven primarily by investment funds, and this is in contrast to pre-crisis years where the growth was driven by SFVs and other off-balance sheet funding vehicles or conduits. The narrow measure represents 27.9% of MUNFI and 13.7% of total global financial assets. Those assets are concentrated in only six jurisdictions (FSB, [2019], ibid., p. 5). See for an evaluation of the narrowing down process, pp. 40 ff. and Annex 1.

FSB, (2019), ibid., pp. 16–21.

See for a detailed and jurisdictional breakdown of the OFI sector, FSB, (2019), ibid. Exh. 2–8, pp. 18–21. Subsegments include investment funds, captives and moneylenders, broker-dealers, MMFs, hedge funds, trust companies, finance companies, real estate trust and funds and CCPs.

For details, see FSB, (2019), ibid., pp. 50–56.

For details, see FSB, (2019), ibid., pp. 56–60.

FSB, (2019), ibid., pp. 46–48.

For details, see FSB, (2019), ibid., pp. 60–64.

For details, see FSB, (2019), ibid., pp. 64–65.

For details, see FSB, (2019), ibid., pp. 65–67.

FSB, (2019), ibid., pp. 22–23.

FSB, (2019), ibid., pp. 23–24.

FSB, (2019), ibid., pp. 25–27.

Direct through borrowing, lending or investment. Indirect occurs when two entities hold common assets (portfolio overlap) or when the market value of their equity or debt securities move together concurrently (co-movement). Also see G. Kara, et al. (2015), Taxonomy of Studies on Interconnectedness, FEDS Notes, July.

Four combinations exist: banks are exposed to OFIs; banks use OFI funding; OFIs use bank funding; and OFIs are exposed to banks. Also see T. Fong, et al. (2018), Assessing the Interconnectedness Between Cross-Border Shadow Banking Systems, HKIMR Working Paper, Nr. 05, July.

FSB, (2019), ibid., pp. 29–39. Also see the case study on cross-border movements between NBFI systems (pp. 83–87). The case study concludes that OFI assets can move together, notably in times of global stress. In recent years, this co-movement was also observed in low VIX periods (periods of low market stress). This seems to relate to lower investors’ expected investment return, stronger demand from institutional investors and funding support from the banking sector. There are some regulatory and policy implications (pp. 86–87).

See for data: FSB, (2019), ibid., p. 30 Exh. 3.2.

FSB, (2019), ibid. Case Study: The Use of CDS by Non-bank FIs in the EU, pp. 88–91. Also see I. Aldasoro and T. Ehlers (2018), The Credit Default Swap Market: What a Difference a Decade Makes, BIS Quarterly Review, June, pp. 1–14; A. Braunsteffer et al. (2019), The Use of CDS by UCITS Investment Funds – Evidence from Regulatory Data, ESRB Working Paper Nr. 95, June; M. D’Errico, et al. (2018), How Does Risk Flow in the Credit Default Swap Market?, Journal of Financial Stability, Vol. 35, April, pp. 53–74; C. Guagliano and J. Mazzacurati, (2018), Drivers of CDS Usage by EU Investment Funds, ESMA Report on Trends, Risks and Vulnerabilities, Nr. 2, September, pp. 66–75; W. Jiang and Z. Zhu, (2016), Mutual Fund Holdings of Credit Default Swaps: Liquidity, Yield, and Risk Taking, Columbia Business School Research Paper, Nr. 15–9; D. Wang et al., (2018), Dynamic Portfolio Overlap Networks, Contagion, and the Credit Spread Puzzle, Working Paper, January 16, mimeo.

For 2017 that implies: Total financial assets (USD 383 trillion)>MUNFI (USD 184 trillion)>OFIs (USD 117 trillion)>Narrow measure (USD 52 trillion); FSB, (2019), ibid., pp. 7, 13 (Exh. 2.1).

See FSB, (2017), Global Shadow Banking Monitoring Report 2016, May 10, Annex 6, pp. 84–86.

See in detail FSB, (2017), Global Shadow Banking Monitoring Report 2016, May 10, Annex 3, pp. 73–76.

For details over the strained relationship between illiquid assets and open-ended investment funds, see Financial Conduct Authority, (2017), Illiquid Assets and Open-Ended Investment Funds, Discussion Paper DP17/1.

See in detail: FSB, (2017), Global Shadow Banking Monitoring Report 2016, May 10, Annex 5, pp. 81–83.

See for a full review of challenges: ESRB, (2016), Macroprudential Policy beyond Banking: An ESRB Strategy Paper, July; ESRB, (2017), A Review of Macroprudential Policy in the EU in 2016, April, as well as further annual updates.

In this methodology, haircuts are applied to assets to protect against potential adverse price movements in stressed market conditions. Although the methodology is designed for totally different purposes (and continuously monitored by the European Banking Authority or EBA) it provides for a very good proxy when it comes to the liquidity (and changes in liquidity) in the portfolio’s held by open-ended funds. It is referred to as a proxy, as the suggested haircuts might not always be sufficient in all potential stress scenarios. The haircuts vary from 0% (for cash and cash equivalents and certain debt securities covered by governments, central banks and public institutions with a credit rating of at least AA-), 15% (for debt securities guaranteed by sovereigns, central banks, etc. with a credit rating between A= and A- and corporate debt issues by institutions beyond financial institutions with a credit rating of at least AA-) and 50% (for corporate debt securities issued by parties beyond financial institutions with a credit rating between A+ and BBB- and certain common equity shares).

MMFs are excluded from the analysis given the stringent quality condition imposed on them by the European directive: Directive 2009/65/EC of the European Parliament and of the Council of 13 July 2009.

See in detail: FSB, (2017), Global Shadow Banking Monitoring Report 2016, May 10, Annex 7, pp. 87–90. See also a follow-up case study: FSB, (2019), Global Monitoring Report on Non-bank Financial Intermediation 2018, ibid., pp. 73–78.

The specifics/technicalities of what constitutes leveraged loans might differ across jurisdictions. See in detail: ECB, (2017), Guidance on Leveraged Transactions, May. Also for the US: FRB, (2013), Interagency Guidance on Leveraged Lending, March.

High-yield bonds are corporate bonds with a sub-investment grade rating.

See, for example, B. Becker and V. Ivanshina, (2016), Covenant-Light Contracts and Creditor Coordination, Riksbank Research Paper Series Nr. 149, December.

J. Cizel et al., (2016), Effective Macroprudential Policy: Cross-Sector Substitution from Price and Quantity Measures, IMF Working Paper Series, Nr. WP/16/94.

S. Kim et al., (2016), Did the Supervisory Guidance on Leveraged Lending Work?, May 16, Liberty Street Economics (newyorkfed.​org).

IOSCO, (2017), IOSCO Research Report on Financial Technologies (Fintech), February. Also: FSB, (2017), Artificial Intelligence and Machine Learning in Financial Services: Market Developments and Financial Stability Implications, November. The applications of Fintech in shadow banking can include the following: crowdfunding used to raise mortgage down payments, where an electronic platform allows prospective homebuyers to raise money for a down payment on a mortgage; crypto-asset-based lending, where a nonbank provides loans that are collateralized by crypto-assets. In one case, such loans were granted to purchase ‘mining equipment’ (computers that allow users to solve algorithms to mine crypto-assets); or tokenized funds or investment funds that issue proprietary tokens to investors and invest the proceeds in various assets, such as real estate development loans or digital assets. See FSB, (2019), ibid., p. 12.

FSB, (2018), Global Shadow Banking Monitoring Report 2017, March 5, Box 1–1, pp. 9–10.

A. Samitsu, (2017), Structure of P2P Lending and Investor Protection, Bank of Japan Research Laboratory Series, October.

See for an in-depth analysis: Annex A3.1 in FSB, (2018), ibid., pp. 75–79. Footnote 114 (p. 75) refers to the different authors who contributed to the analysis.

See recently: Y. S. Schüler et al., (2015), Characterizing the Financial Cycle: A Multivariate and Time-Varying Approach, ECB Working Papers Series, Nr. 1846, September.

See for actual data: BIS total credit statistics (via bis.​org), update on a quarterly basis. See also and for alternative models: C. Dembiermont, et al., (2013), How Much Does the Private Sector Really Borrow? A New Database for Total Credit to the Private Non-Financial Sector, BIS Quarterly Review, March, pp. 65–81; J. Cizel, et al., (2016), Effective Macroprudential Policy: Cross-Sector Substitution from Price and Quantity Measures, IMF Working Paper, Nr. WP/16/94, April.

See also the follow-up study performed in this respect: FSB, (2019), ibid., pp. 79–82 and literature references made. Also see E. Kemp, et al. (2018), The Non-Bank Credit Cycle, FEDS Working Paper Nr. 2018–076, October.

This often is the consequence of in-house banks (part of OFI) channeling funds through that country and so the volumes also reflect up to a certain point the investment infrastructure of a particular country.

Those models are not discussed here but see: for the different models possible: L. Christiano, and T. Fitzgerald, (2003), The Band-Pass Filter, International Economic Review, Vol 44, Issue 2, May, pp. 435–465; G. Farrell and E. Kemp, (2017), Measuring the Financial Cycle in South Africa, November 23, Working Paper, mimeo; D. Aikman, et al., (2015), Curbing the Credit Cycle, Economic Journal, Vol. 125, Issue 585, June, pp. 1072–1109. It should be stressed that we are only in the early stages of developing models to understand nonbank credit cycles and even more so the synchronization of both bank and nonbank credit cycles. Filtering out the impact of a certain financial infrastructure, the link between monetary policy and the aforementioned synchronization, and indicative and predictive values of those cycles for future periods of distress are all topics on which material progress needs to be made.

See Annex 3 (Corporate cash holdings as a demand factor for nonbank financial instruments) in FSB, (2018), ibid., pp. 79–84.

Most shadow banking research focuses on the supply side of nonbanks.

This sounds very much like the discussion we had elsewhere regarding the demand for safe and liquid assets by institutional cash pools.

Some cash pools are so huge that firms decide to develop their own asset management firm in-house to manage that cash: see, for example, Apple and Braeburn Capital managing over 250 billion in AUM. See FSB, (2018), ibid., pp. 79–81, for the evolution of corporate cash balances in market data terms. It is interesting that the trend is similar for listed and nonlisted corporations.

FSB, (2018), ibid., p. 79. Pre-crisis corporations reduced their cash allocation into bank instruments relative to nonbank instruments. After the crisis, corporates increased the allocation of their cash to bank instruments more than to nonbank instruments (pp. 82–83). Corporates seem to favor direct investment over nonbank product. MMFs make up about half of non-deposit holdings.

They started to use a variety of other existing products however: ‘separately managed accounts; money market demand accounts (a structured bank deposit product that offers a higher interest rate in return for certain restrictions) and other structured bank deposit products; repo and bank collateral products (e.g. direct repos, evergreen repos); bond and cash-strategy ETFs; ultra-short funds; and dynamic discounting (supplier discounts for early payment of accounts payable)’: FSB, (2018), ibid., p. 84.

See Bank of England, (2017), Financial Stability Report, Issue 41, June; C. Crowe et al., (2011), How to Deal with Real Estate Booms: Lessons from Country Experiences, IMF Working Paper, Nr. WP/11/91, April; S. Fisher, (2018), Housing and Financial Stability, Federal Reserve Board of Governors, speech, June, 20. See for an overview: Annex A3.3: Developments and Adaptations in the Housing Finance Markets in FSB, (2018), ibid., pp. 85–87. Remarkable observations in terms of nonbank holdings in mortgages: Canada 11.3%: D. Coletti, et al., (2016), The Rise of Mortgage Finance Companies in Canada: Benefits and Vulnerabilities, Financial System Review, December, pp. 39–52; India: 46.5% through housing finance companies; the Netherlands: 14.4% through pension funds and insurance firms; the UK: 8.6%; the US: 7.9%

The underwriting business models vary across jurisdictions: see FSB, (2018), ibid., pp. 87–88 for some examples.

See for the trend in the Netherlands: J. Kakes et al., (2017), Verschuivingen in de financiering van hypotheekschuld, Economisch Statistische Berichten, May 11, pp. 69–73.

M. Richter and J.G. Werner, (2016), Conceptualising the Role of International Capital Flows for Housing Markets, Intereconomics, May, Vol. 51, Issue 3, pp. 146–154. Regulation might incentivize cross-border lending: D. Reinhardt and R. Sowerbutts, (2015), Regulatory Arbitrage in Action: Evidence from Banking Flows and Macroprudential Policy, BoE Staff Working Paper, Nr. 546, September.

S. Pool, (2017), Mortgage Debt and Shadow Banks, Presentation November 11, (via bankofgreece.gr); E. Cerutti, et al., (2015), The Use and Effectiveness of Macroprudential Policies: New Evidence, IMF Working Paper, Nr. WP/15/65, March; E. Cerutti et al., (2016), Changes in Prudential Policy Instruments – A New Cross-Country Database, IMF Working Paper, Nr. WP/16/110, June. Also see ESRB, (2016),: Macroprudential Policy Beyond Banking: an ESRB Strategy Paper, July.

J. M. Serena and B. Tissot, (2017), Data Needs and Statistics Compilation for Macroprudential Analysis, Irving Fisher Committee on Central Bank Statistics (IFC) Bulletin, Nr. 46, December, pp. 1–12.

See FSB, (2018), ibid., pp. 90–91 for an overview of realizations per country.

Also see IOSCO (2017): Findings of the Survey on Loan Funds, FR03/2017, February. They distinguish between different types of loan funds: (1) a loan originating fund and (2) a loan participating fund. There are funds like the EU’s alternative investment funds (AIFs) that invest in a variety of credit products. There are two general fund frameworks in the EU: the undertakings for collective investment in transferable securities (UCITS) model and the alternative investment fund managers directive (AIFMD) model. AIMFD covers what is not within scope at the level of the UCITS regulation. AIFs are part of the AIMFD framework. ‘Under EU rules AIFs can originate loans without additional diversification or investment limits, unless they are also authorised under a specific EU common legislation such as the European Venture Capital Fund (EuVECA), the European Social Entrepreneurship Fund (EuSEF) and the European Long Term Investment Fund (ELTIF) Regulations’: FSB, (2018), ibid., p. 95. Loan origination is often possible given portfolio diversification and investment restrictions.

See FSB, (2018), ibid., p. 93. For their performance and standard setting, see: ESMA, (2016), Key Principles for a European Framework on Loan Origination by Funds, April 11, and IOSCO, (2017), ibid. See for a comparative analysis of direct lending by funds in the EU: Debevoise and Plimpton, (2017), Direct Lending by Funds: A Comparison of the Key EU Jurisdictions, May 23, pp. 1–11. It needs to be observed that on top of EU law there are national requirements: (1) many jurisdictions required loan origination funds to be structured as closed-end funds, (2) the potential use of leverage in loan origination funds is limited, especially in case marketed to retail investors, and (3) concentration limits so that not more than often 10% of funding can be invested in a single loan asset.