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Journal of Economic Interaction and Coordination
Erschienen in: Journal of Economic Interaction and Coordination 2/2018

03.11.2016 | Regular Article

A survey of network-based analysis and systemic risk measurement

verfasst von: Andre R. Neveu

Erschienen in: Journal of Economic Interaction and Coordination | Ausgabe 2/2018

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Abstract

The financial crisis led to a number of new systemic risk measures and a renewed concern over the risk of contagion. This paper surveys the systemic risk literature with a focus on the importance of contributions made by those emphasizing a network-based approach, and how that compares with more commonly used approaches. Research on systemic risk has generally found that the risk of contagion through domino effects is minimal, and thus emphasized focusing on the resiliency of the financial system to broad macroeconomic shocks. Theoretical, methodological, and empirical work is critically examined to provide insight on how and why regulators have emphasized deregulation, diversification, size-based regulations, and portfolio-based coherent systemic risk measures. Furthermore, in the context of network analysis, this paper reviews and critically assesses newly created systemic risk measures. Network analysis and agent-based modeling approaches to understanding network formation offer promise in helping understand contagion, and also detecting fragile systems before they collapse. Theory and evidence discussed here implies that regulators and researchers need to gain an improved understanding of how topology, capital requirements, and liquidity interact.
Vorheriger Artikel Competitive moment matching of a New-Keynesian and an Old-Keynesian model
Nächster Artikel Band or point inflation targeting? An experimental approach

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Fußnoten
1
Subsequent work by Caballero and Simsek (2013), Jorion and Zhang (2009) and European Central Bank (2009) further describe the issues presented by Yellen (2013).
 
2
Two prominent examples of how network models are glossed over include the Chan-Lau et al. (2009) Global Financial Stability Review which discusses networks using aggregated rather than firm-level data, and Bisias et al. (2012) who discusses network models using mostly reduced form estimation methods and aggregated data.
 
3
General equilibrium models are differentiated from those which explore financial markets using network theory focusing on topology and evolution without resorting to a general equilibrium or representative agent framework. Theoretical network models often use simple behavioral foundations which assume no feedback effects. Bargigli and Tedeschi (2014) describes these models as having global interaction, where actors behavior depends on that of all others. Agent-based network models go a step further by allowing interaction to affect behavior through local network feedback and adaptive behavior by economic actors.
 
4
There are many taxonomies of systemic risk, enough that Borio and Drehmann (2009) dedicate significant space to discussing a brief list of papers describing risk taxonomies.
 
5
Thurner (2011) provides a discussion on the limitations of agent-based models as it relates to systemic risk. Bargigli et al. (2014) provides one recent example of a calibrated agent-based network model.
 
6
Relabeling changes in the number of market participants as a only a network externality would be a mistake since the agents are usually able to internalize the benefits or losses. Nor should endogenous risk be labeled solely as a pecuniary externality—although those may exist in incomplete markets—since it can be the nature of the network that amplifies a possibly very small initial shock.
 
7
A few notable multiplex research studies are listed in Table 5.
 
8
Hasman (2013) provides a brief overview of research on the risk of contagion in the banking industry.
 
9
Montagna and Kok (2013) represents one recent effort to help detect systemically important nodes using a network modeling approach.
 
10
The groups in Bisias et al. (2012) data requirements taxonomy are: macroeconomic measures; granular foundations and network measures; forward-looking risk measures; stress-test measures; cross-sectional measures; and measures of illiquidity and insolvency. They also provide three other taxonomies for these same 31 measures, grouping them by time horizon; supervisory scope; and research method. While we discuss some of these 31 measures here, we refer readers to their work for further discussion.
 
11
Notable exceptions are work by Huang et al. (2013) and Levy-Carciente et al. (2015) who examine balance sheet data in the US and Venezuela respectively. Squartini et al. (2013) notes that their methods require only a map of connections rather than dollar values, and offer some hope that comprehensive data requirements are not necessary.
 
12
Herding is often depicted as a broad economic shock which fails to consider who is part of a herd or why.
 
13
Allen and Babus (2009), and De Bandt provide an overview of some earlier theoretical and empirical network research on financial markets.
 
14
Allen and Gale (2000) do note that incomplete markets with low connectivity have little risk of contagion since firm liquidity is not linked. Low connectivity in incomplete markets leaves isolated firms at a greater risk of failure.
 
15
The financial accelerator might best be described as a shock being amplified when financial conditions deteriorate for a firm and they are subsequently less able to secure necessary loans or revenue in the future. The accelerator creates a viscous feedback loop where investment declines because of reduced internal/external funding, which decreases output, future revenue, and collateral values. Financial accelerators were introduced by Bernanke et al. (1999), and played a major role in the explanation of the monetary policy response to the financial crisis (Bernanke and Gertler 2010).
 
16
Tedeschi et al. (2012) provides a simulation test of a model similar to Battiston et al. (2012b) and Riccetti et al. (2013), also finding a robust-yet-fragile topography.
 
17
Furfine (2003) developed a sequential algorithm to estimate the impacts of contagion in interbank markets. The contagion risk considered by Furfine (2003) is limited to a one-way cascade (Upper 2011). For example, if one bank failure leads to a second bank failure, a sequential algorithm ignores secondary losses that the first bank may incur. Sequential algorithms can vastly understate the potential costs of contagion.
 
18
Cont et al. (2013) creates a Contagion Index (CI), a conditional measure based on exposures which can be applied to individual institutions to estimate systemic risk. Cont et al. (2013) also simulate default contagion by assuming short-run losses are complete in the case of a default. This method deviates from the approach of Eisenberg and Noe (2001) who assume losses are quickly calculated and remaining debts are easily recoverable. Cont et al. (2013) suggest targeting capital requirements at the riskiest firms, a proposal already under consideration by many regulators. The theoretical models employed by Cont et al. (2013) were developed in Amini et al. (2010) and Amini et al. (2011). Mistrulli (2011) uses a similar method to Cont et al. (2013) by departing from the maximum entropy approach used by others. It is worth reiterating that the maximum entropy approach likely misstates the level of systemic risk.
 
19
Microprudential regulation is aimed at preventing the failure of individual financial institutions, while macroprudential regulation is a focused “effort to control the social costs associated with excessive balance-sheet shrinkage on the part of multiple financial institutions hit with a common shock” (Hanson et al. 2011). (Emphasis in original.)
 
20
Upper (2011) provides a breakdown of 15 studies using sequential and EN algorithms to estimate the risk of contagion, and summarizes that the greatest systemic risk is due to correlated default rather than domino effects.
 
21
Agent-based models such as Bluhm et al. (2013) are discussed further in Sect. 3.3.
 
22
Hüser (2015) provides a thorough list of empirical and theoretical papers on interbank networks.
 
23
Bech and Atalay (2010) also provide insight on the US interbank network, showing the system is directed in such a way that surplus reserves are typically lent from small banks, to regional banks, and then on to money center banks in New York, Boston, or Chicago. Hernández et al. (2010) and Hale (2011) provide similar evidence using co-lending data to show US network structures are highly dynamic in response to shocks and do not rule out the potential risk for contagion.
 
24
Mastromatteo et al. (2012), Squartini et al. (2013), and Anand et al. (2015) also explore alternatives and criticisms of maximum entropy methods.
 
25
Emphasis in original. Also see Iori et al. (2006) and Tedeschi et al. (2012) for similar findings.
 
26
Preliminary work by Pegoraro (2012) examines a number of scenarios including targeted attacks at different types of networks (e.g., random, small-world, and scale-free). The approach by Pegoraro (2012) includes the use of network statistics which imply susceptibility to attack and contagion.
 
27
Teteryatnikova (2014) provides a theoretical model showing similar effects of tiering on system stability.
 
28
Hałaj and Kok (2015) similarly look at a model of endogenous network formation, but utilize an optimizing agent approach rather than bounded rationality agent-based methods. Iori et al. (2006) is not billed as an agent-based model, but is repeatedly used as a baseline approach to network design.
 
29
The FSOCs proposed rules require firms to be identified in the first stage as those which exceed one or more of the following thresholds: more than $30 billion in credit default swaps outstanding; $3.5 billion in derivative liabilities; $20 billion in loans and bonds outstanding; leverage of greater than 15-to-1; and a short-term debt ratio of more than 10% (Financial Stability Oversight Council 2011).
 
30
A typical firm’s VaR would be an estimate of the potential losses given market returns at the bottom 5th or 1st percentile over a given time horizon based on historical data.
 
31
Expected shortfall (ES) measures are weighted averages under a range of VaR measures, effectively integrating over the probability distribution used to estimate losses conditional on a certain VaR threshold being breached. Adrian and Brunnermeier (2009) provide a derivation of the difference between VaR and ES.
 
32
Aragonés et al. (2008) also discuss spectral risk measures and probable maximum losses which take closer account of a firm’s risk aversion and use extreme value theory often employing functions such as generalized Pareto distributions. The authors discuss how risk committees should assign probabilities to extreme events under certain forward-looking scenarios. Subjective scenarios are often overlooked due to the lack of historical evidence and the lack of senior management involvement in the risk management process. Thus, we might think of the consideration of home prices falling in the recent crisis as subjective scenario that was likely discounted for lack of historical evidence or the belief that such an event was simply implausible.
 
33
The MES is estimated as the expected value of losses when the market return is below a particular percentile during a given time frame, such as the 5th percentile. The SES measure is essentially a combination of the MES and leverage. Twice a year, the ECB releases the Financial Stability Review which examines possible sources of risk to financial stability.
 
34
Huang et al. (2009) developed the distress insurance premium (DIP), which is similar to the MES, but also incorporates information on CDS and equity prices. Segoviano and Goodhart (2009) developed three measures—the banking stability index (BSI), the joint probability of default (JPoD)), and distress between banks (DiDe)—utilizing CDSs, out of the money option prices, and sovereign debt holdings. Zhou (2010) proposes two measures: a vulnerability index (VI) which estimates how vulnerable a given bank is to other bank failures in the system; and the systemic impact index (SII) intended to capture the level of financial institution interconnectivity measuring the expected number of failures that would occur given a particular institution fails. Billio et al. (2010) use publicly traded equity returns as a proxy for illiquidity to provide evidence that systemic risk might originate outside the financial sector. Including sectors that are not purely financial, linear Granger causality tests by Billio et al. (2010) provide some statistical evidence that periods of distress have occurred after the fact.
 
35
The CCA estimates market-implied contingent liabilities, through a combination of financial market data and accounting information to estimate risk-adjusted balance sheets (Gray and Jobst 2010). Gray and Jobst (2010) note that the CoVaR and SES measures are only quarterly, while the DIP and systemic CCA measures can be estimated daily.
 
36
Emphasis in original.
 
37
Markose et al. (2010) is one example of an agent-based network model of financial markets and regulation.
 
38
Bargigli and Tedeschi (2014) offers a survey of the literature on the agent-based approach in network modeling.
 
39
Coherent risk measures are also monotonic in the sense that an asset with higher losses at all outcomes has a higher risk measure, and the addition of cash reduces the risk of a portfolio dollar for dollar (Artzner et al. 1999).
 
40
Cont et al. (2010) cites previous work by Ibragimov and Walden (2007) that shows diversification may not decrease the risk of large tail events.
 
41
di Iasio et al. (2013) perform a similar stress test of the Italian e-MID interbank market using the DebtRank algorithm.
 
42
Knightian risk and uncertainty can be grouped together as both being incomputable if one agrees with Taleb (2010) that real world probabilities and parameters used to estimate Knightian risk are unknown.
 
43
Battiston and Caldarelli (2013) clearly makes this point. Allen and Gale (2003) discuss the theoretical shortcomings as applied to capital requirements, while often advocating for looser capital requirements.
 
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Metadaten
Titel
A survey of network-based analysis and systemic risk measurement
verfasst von
Andre R. Neveu
Publikationsdatum
03.11.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
Journal of Economic Interaction and Coordination / Ausgabe 2/2018
Print ISSN: 1860-711X
Elektronische ISSN: 1860-7128
DOI
https://doi.org/10.1007/s11403-016-0182-z

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