Skip to main content
SpringerLink
Log in

A survey of network-based analysis and systemic risk measurement

  • Regular Article
  • Published:
Journal of Economic Interaction and Coordination Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

Notes

  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.

References

  • Acemoglu D (2012) Systemic risk: insights from networks. AFA presentation, 6 Jan 2012

  • Acharya VV, Pedersen LH, Philippon T, Richardson MP (2010) Measuring systemic risk. Working paper 1002 Federal Reserve Bank of Cleveland

  • Adrian T, Brunnermeier MK (2009) CoVaR. Federal Reserve Bank of New York Staff Report 348. New York Federal Reserve, New York

  • Aikman D, Alessandri P, Eklund B, Gai P, Kapadia S, Martin E, Mora N, Sterne G, Willison M (2009) Funding liquidity risk in a quantitative model of systemic stability. Bank of England working paper 372, Bank of England

  • Allen F, Babus A (2009) Networks in finance. In: Kleindorfer PR, Wind Y, Gunther RE (eds) The network challenge: strategy, profit, and risk in an interlinked world. Wharton School Publishing, Upper Saddle River, pp 367–382

    Google Scholar 

  • Allen F, Carletti E (2013) New theories to underpin financial reform. J Financ Stab 9:242–249

    Google Scholar 

  • Allen F, Gale D (2000) Financial contagion. J Polit Econ 108(1):1–33

    Google Scholar 

  • Allen F, Gale D (2003) Capital adequacy regulation: in search of a rationale. In: Arnott R, Greenwald B, Kanbur R, Nalebuff B (eds) Economics for an imperfect world: essays in honor of Joseph Stiglitz. MIT Press, Cambridge, pp 83–109

    Google Scholar 

  • Allen F, Gale D (2004) Financial fragility, liquidity, and asset prices. J Eur Econ Assoc 2(December):1015–1048

    Google Scholar 

  • Allen F, Gale D (2006) The risks of financial institutions. In: Carey M, Stulz RM (eds) Systemic risk and regulation. The University of Chicago Press, Chicago, pp 341–375 IL, Ch. 7

    Google Scholar 

  • Amini H, Cont R, Minca A (2010) Resilience to contagion in financial networks. SSRN working paper 1865997

  • Amini H, Cont R, Minca A (2011) Stress testing the resilience of financial networks. Int J Theor Appl Finance 15(1):1–20

    Google Scholar 

  • Anand K, Craig B, von Peter G (2015) Filling in the blanks: network structure and interbank contagion. Quant Finance 15(4):673–691

    Google Scholar 

  • Aragonés J R, Blanco C, Dowd K (2008) Stress testing for financial institutions. In: Rösch D, Scheule H (eds) Stress tests, market risk measures and extremes: bringing stress tests to the forefront of market risk management. Risk Books, London, pp 17–34 Ch. 2

    Google Scholar 

  • Artzner P, Delbaen F, Eber J-M, Heath D (1999) Coherent measures of risk. Math Finance 9(3):203–208

    Google Scholar 

  • Babus A (2005) Financial development, integration and stability. In: Liebscher K (ed) Contagion risk in financial networks. Edward Elgar, Cheltenham, pp 423–440

    Google Scholar 

  • Babus A (2007) The formation of financial networks Tinbergen Institute discussion paper No. 2006-093/2, FEEM working paper No. 69.2007. http://ssrn.com/abstract=939754, pp 1–32

  • Bardoscia M, Battiston S, Caccioli F, Caldarelli G (2015) DebtRank: a microscopic foundation for shock propagation. http://arxiv.org/abs/1504.01857

  • Bargigli L, di Iasio G, Infante L, Lillo F, Pierobon F (2015) The multiplex structure of interbank networks. Quant Finance 15(4):673–691

    Google Scholar 

  • Bargigli L, Gallegati M, Riccetti L, Russo A (2014) Network analysis and calibration of the “leveraged network-based financial accelerator”. J Econ Behav Org 99:109–125

    Google Scholar 

  • Bargigli L, Tedeschi G (2014) Interaction in agent-based economics: a survey on the network approach. Phys A 399:1–15

    Google Scholar 

  • Battiston S, Caldarelli G (2013) Systemic risk in financial networks. J Financ Manag Mark Inst 1(2):129–154

    Google Scholar 

  • Battiston S, Delli Gatti D, Gallegati M, Greenwald B, Stiglitz JE (2012a) Default cascades: when does risk diversification increase stability? J Financ Stab 8(3):138–149

    Google Scholar 

  • Battiston S, Delli Gatti D, Gallegati M, Greenwald BC, Stiglitz JE (2012b) Liaisons dangereuses: increasing connectivity, risk sharing, and systemic risk. J Econ Dyn Control 36(8):1121–1141

    Google Scholar 

  • Battiston S, Puliga M, Kaushik R, Tasca P, Caldarelli G (2012c) DebtRank: too central to fail? Financial networks, the fed and systemic risk. Sci Rep 2(541):1–6

    Google Scholar 

  • Battiston S, Caldarelli G, D’Errico M, Gurciullo S (2015) Leveraging the network: a stress-test framework based on DebtRank. SSRN working paper 2571218

  • Battiston S, Farmer JD, Flache A, Garlaschelli D, Haldane AG, Heesterbeek H, Hommes C, Jaeger C, May R, Scheffer M (2016) Complexity theory and financial regulation. Science 351(6275):818–819

    Google Scholar 

  • Bech ML, Atalay E (2010) The topology of the federal funds market. Phys A 389(22):5223–5246

    Google Scholar 

  • Bernanke BS, Gertler M (2010) Inside the black box: the credit channel of monetary policy transmission. J Econ Perspect 9(4):27–48

    Google Scholar 

  • Bernanke BS, Gertler M, Gilchrist S (1999) The financial accelerator in a quantitative business cycle framework. In: Taylor JB, Woodford M (eds) Handbook of macroeconomics. North-Holland, Amsterdam, pp 1341–1393

    Google Scholar 

  • Billio M, Getmansky M, Lo AW, Pelizzon L (2010) Econometric measures of systemic risk in the finance and insurance sectors. MIT Sloan School working paper 4774-10, MIT Sloan School of Management, Cambridge, MA

  • Bisias D, Flood M, Lo A, Valavanis S (2012) A survey of systemic risk analytics. Office of Financial Research working paper 0001, US Department of the Treasury, Washington, DC

  • Blåvarg M, Nimander P (2002) Interbank Exposures and Systemic Risk, In: Bank of International Settlements (ed) Risk measurement and systemic risk, proceedings of the third joint central bank research conference, October 2002. Bank for International Settlements, pp 287–305

  • Bluhm M, Faia E, Krahnen JP (2013) Endogenous banks networks, cascades and systemic risk. SAFE working paper Series Number 12, Center of Excellence SAFE Sustainable Architecture for Finance in Europe

  • Blume L, Easley D, Kleinberg J, Kleinberg R, Tardos E (2011) Network formation in the presence of contagious risk. In: Proceedings of the 12th ACM conference on electronic commerce, pp. 1–23

  • Borio C, Drehmann M (2009) Towards an operational framework for financial stability: “fuzzy” measurement and its consequences. BIS working papers: monetary and economic development Number 284, Bank for International Settlements

  • Boss M, Elsinger H, Summer M, Thurner S (2004) Network topology of the interbank market. Quant Finance 4(6):677–684

    Google Scholar 

  • Boss M, Krenn G, Puhr C, Summer M (2006) Systemic risk monitor: a model for systemic risk analysis and stress testing of banking systems. OeNB Financ Stab Rep 11:83–95

    Google Scholar 

  • Bouchaud J-P (2009) The (unfortunate) complexity of the economy. Phys World 22(4):28–32

    Google Scholar 

  • Brock WA, Hommes CH, Wagener FO (2009) More hedging instruments may destabilize markets. J Econ Dyn Control 33(11):1912–1928

    Google Scholar 

  • Brownlees CT, Engle R (2011) Volatility, correlation and tails for systemic risk measurement. SSRN working paper 1611229

  • Brusco S, Castiglionesi F (2007) Liquidity coinsurance, moral hazard, and financial contagion. J Finance 62(5):2275–2302

    Google Scholar 

  • Caballero RJ, Simsek A (2013) Fire sales in a model of complexity. J Finance 68(6):2549–2587

    Google Scholar 

  • Caccioli F, Catanach TA, Farmer JD (2012) Heterogeneity, correlations and financial contagion. Adv Complex Syst 15(s2):1–15

    Google Scholar 

  • Cappiello L, Kadareja A, Sørensen CK, Protopapa M (2010) Do bank loans and credit standards have an effect on output? A panel approach for the Euro Area. ECB working paper Series No. 1150, European Central Bank, Frankfurt, Germany

  • Castiglionesi F, Navarro N (2008) Optimal fragile financial networks second Singapore international conference on finance 2008, EFA 2008 Athens meetings paper. Available at SSRN: http://ssrn.com/abstract=1089357, pp 1–36

  • Chan-Lau J, Espinosa-Vega MA, Giesecke K, Solé J (2009) Assessing the systemic implications of financial linkages. In: International Monetary Fund (ed) Global financial stability report. chap 2. International Monetary Fund, Washington DC, pp 73–110

  • Chen H, Cummins JD, Viswanathan KS, Weiss MA (2013) Systemic risk and the interconnectedness between banks and insurers: an econometric analysis. J Risk Insur 81(3):623–652

    Google Scholar 

  • Cifuentes R, Ferrucci G, Shin H (2005) Liquidity risk and contagion. J Eur Econ Assoc 3(2/3):556–566

    Google Scholar 

  • Chinazzi M, Fagiolo G (2013) Systemic risk, contagion, and financial networks: a survey. Available at SSRN: http://ssrn.com/abstract=2243504

  • Cocco JaF, Gomes FJ, Martins NC (2009) Lending relationships in the interbank market. J Financ Intermed 18(1):24–48

    Google Scholar 

  • Cont R, Deguest R, Scandolo G (2010) Robustness and sensitivity analysis of risk measurement procedures. Quant Finance 10(6):593–606

    Google Scholar 

  • Cont R, Kan YH (2011) Statistical modeling of credit default swap portfolios. http://ssrn.com/abstract=1771862, pp 1–43

  • Cont R, Moussa A, Bastos e Santos E (2013) Handbook of systemic risk. In: Fouque JP, Langsam J (eds) Network structure and systemic risk in banking systems. Cambridge University Press, Cambridge, pp 327–368

  • Craig BR, von Peter G (2014) Interbank tiering and money center banks. J Financ Intermed 23(3):322–347

    Google Scholar 

  • Daníelsson J, Jorgensen BrN, Sarma M, de Vries CG (2006) Comparing downside risk measures for heavy tailed distributions. Econ Lett 92:202–208

    Google Scholar 

  • Daníelsson J, Shin HS (2003) Endogenous risk. In: Field P (ed) Modern risk management–a history. Risk Books, London

    Google Scholar 

  • Daníelsson J, Shin HS, Zigrand JP (2013) Quantifying systemic risk. In: Haubrich JG, Lo AW (eds) Endogenous and systemic risk. university of chicago press, Chicago, pp 73–94

    Google Scholar 

  • Daníelsson J, Zigrand JP (2012) Endogenous extreme events and the dual role of prices. Annu Rev Econ 4:111–129

    Google Scholar 

  • Dasgupta A (2004) Financial contagion through capital connections: a model of the origin and spread of financial panics. J Eur Econ Assoc 2(6):1049–1084

    Google Scholar 

  • De Bandt O, Hartmann P, Peydró JL (2010) Systemic risk in banking: an update. In: Berger AN, Molyneux P, Wilson J (eds) The Oxford handbook of banking. Oxford University Press, Oxford, pp 633–672 Ch. 25,

    Google Scholar 

  • Delli Gatti D, Gallegati M, Greenwald B, Russo A, Stiglitz JE (2010) The financial accelerator in an evolving credit network. J Econ Dyn Control 34(9):1627–1650

    Google Scholar 

  • Delpini D, Battiston S, Riccaboni M, Giampaolo G, Pammolli F, Caldarelli G (2013) Evolution of controllability in interbank networks. Sci Rep 3(1626):1–5

    Google Scholar 

  • Degryse H, Nguyen G (2007) Interbank exposures: an empirical examination of contagion risk in the belgian banking system. Int J Central Bank 3:123–171

    Google Scholar 

  • di Iasio G, Battiston S, Infante L, Pierobon F (2013) Capital and contagion in financial networks. MPRA working paper No. 52141

  • de Vries CG (2005) The simple economics of bank fragility. J Bank Finance 29(4):803–825

    Google Scholar 

  • Drehmann M (2009) Stress-testing the banking system: methodologies and applications. In: Quagliariello M (ed) Macroeconomic stress-testing banks: a survey of methodologies. Cambridge University Press, Cambridge, pp 37–62 (Ch 3)

    Google Scholar 

  • Drehmann M, Tarashev N (2011) Systemic importance: some simple indicators. BIS Quart Rev :25–37

  • Dudley WC (2011) US experience with bank stress tests. Remarks to the group of 30 plenary meeting 5/28/2011, Federal Reserve Bank of New York, Bern, Switzerland

  • Eisenberg L, Noe TH (2001) Systemic risk in financial systems. Manag Sci 47(603):236–249

    Google Scholar 

  • Elliott M, Golub B, Jackson MO (2014) Financial networks and contagion. Am Econ Rev 104(10):3115–3153

    Google Scholar 

  • Elsinger H, Lehar A, Summer M (2006a) Risk assessment for banking systems. Manag Sci 52(9):1301–1314

    Google Scholar 

  • Elsinger H, Lehar A, Summer M (2006b) Using market information for banking system risk assessment. Int J Central Bank 2(1):137–165

    Google Scholar 

  • Elsinger H, Lehar A, Summer M (2013b) Handbook of systemic risk. In: Fouque JP, Langsam JA (eds) Network models and systemic risk assessment. Cambridge University Press, Cambridge, pp 287–305 (Ch 11)

    Google Scholar 

  • European Central Bank (2009) Credit default swaps and counterparty risk. ECB, Frankfurt

  • European Central Bank (2010a) Financial networks and financial stability. Financial stability review, June edn. European Central Bank, Frankfurt, Germany, pp 155–160

  • European Central Bank (2010b) New quantitative measures of systemic risk. Financial stability review, December edn. European Central Bank, Frankfurt, Germany, pp 147–153

  • Financial Stability Oversight Council (2011) Authority to require supervision and regulation of certain nonbank financial companies, proposed rule. Federal Register October 11 (RIN 4030-AA00)

  • Freixas X, Parigi BM, Rochet J-C (2000) Systemic risk, interbank relations and liquidity provision by the central bank. J Money Credit Bank 32(3):611–638

    Google Scholar 

  • Furfine CH (2003) Interbank exposures: quantifying the risk of contagion. J Money Credit Bank 35(1):111–128

    Google Scholar 

  • Gaffeo E, Molinari M (2015) Interbank contagion and resolution procedures: inspecting the mechanism. Quant Finance 15(4):637–652

    Google Scholar 

  • Gai P, Haldane A, Kapadia S (2011) Complexity, concentration and contagion. J Monet Econ 58(5):453–470

    Google Scholar 

  • Gai P, Kapadia S (2010) Contagion in financial networks. Proc R Soc A: Math, Phys Eng Sci 466(2120):2401–2423

    Google Scholar 

  • Galbiati M, Soramäki K (2012) Clearing networks. J Econ Behav Org 83(3):609–626

    Google Scholar 

  • Georg C (2013) The effect of the interbank network structure on contagion and common shocks. J Bank Finance 37(7):2216–2228

    Google Scholar 

  • Giansante S, Chiarella C, Sordi S, Vercelli A (2012) Structural contagion and vulnerability to unexpected liquidity shortfalls. J Econ Behav Org 83(3):558–569

    Google Scholar 

  • Giesecke K, Weber S (2004) Cyclical correlations, credit contagion, and portfolio losses. J Bank Finance 28(12):3009–3036

    Google Scholar 

  • Gray DF, Jobst AA (2010) Lessons from the financial crisis on modelling systemic and sovereign risk. In: Berd AM (ed) Lessons from the financial crisis. Risk Books, chap 8. London, pp 187–230

  • Gray DF, Merton RC, Bodie Z (2007) New framework for measuring and managing macrofinancial risk and financial stability. NBER working paper 13607, National Bureau of Economic Research, Cambridge, MA

  • Grilli R, Tedeschi G, Gallegati M (2015) Markets connectivity and financial contagion. J Econ Interact Coord 10(2):287–304

  • Hałaj G, Kok C (2013) Assessing interbank contagion using simulated networks. CMS 10(2):157–186

    Google Scholar 

  • Hałaj G, Kok C (2015) Modelling the emergence of the interbank networks. Quant Finance 15(4):653–671

    Google Scholar 

  • Haldane AG (2009a) Rethinking the financial network. Speech to the financial student association. Financial Student Association, Amsterdam

    Google Scholar 

  • Haldane AG (2009b) Why banks failed the stress test, speech. February, Marcus-Evans Conference on Stress Testing, London

  • Haldane AG, May RM (2011) Systemic risk in banking ecosystems. Nature 469(7330):351–355

    Google Scholar 

  • Hale G (2011) Bank relationships, business cycles, and financial crises. J Int Econ 88(2):312–325

    Google Scholar 

  • Hanson SG, Kashyap AK, Stein JC (2011) A macroprudential approach to financial regulation. J Econ Perspect 25(1):3–28

    Google Scholar 

  • Hasman A (2013) A critical review of contagion risk in banking. J Econ Surv 27(5):978–995

    Google Scholar 

  • Heise S, Kuhn R (2012) Derivatives and credit contagion in interconnected networks. Eur Phys J B 85(4):1–19

    Google Scholar 

  • Hernández MA, Ho H, Koutrika G, Krishnamurthy R, Popa L, Stanoi IR, Vaithyanathan S, Das S (2010) Unleashing the power of public data for financial risk measurement, regulation, and governance. IBM technical paper RJ10475

  • Huang X, Zhou H, Zhu H (2009) A framework for assessing the systemic risk of major financial institutions. J Bank Finance 33(11):2036–2049

    Google Scholar 

  • Huang X, Vodenska I, Havlin S, Stanley HE (2013) Cascading failures in bi-partite graphs: model for systemic risk propagation. Sci Rep 3(1219):1–8

    Google Scholar 

  • Hughes T (2012) Would the CCAR catch WaMu? Economic & Consumer Credit Analytics, Moody’s Analytics, West Chester, pp 1–7

  • Hüser A (2015) Too interconnected to fail: a survey of the interbank networks literature. SAFE | sustainable architecture for finance in Europe (working paper) No. 91

  • Iazzetta C, Manna M (2009) The topology of the interbank market: developments in Italy since 1990. Bank of Italy Temi di Discussione (working paper) No. 711

  • Ibragimov R, Walden J (2007) The limits of diversification when losses may be large. J Bank Finance 31(8):2551–2569

    Google Scholar 

  • Inaoka H, Ninomiya T, Taniguchi K, Shimizu T, Takayasu H (2004) Fractal network derived from banking transaction: an analysis of network structures formed by financial institutions. Bank of Japan working paper 04-E-04, Bank of Japan

  • Iori G, De Masi G, Precup OV, Gabbi G, Caldarelli G (2008) A network analysis of the italian overnight money market. J Econ Dyn Control 32(1):259–278

    Google Scholar 

  • Iori G, Jafarey S, Padilla FG (2006) Systemic risk on the interbank market. J Econ Behav Org 61(4):525–542

    Google Scholar 

  • Johnson N (2011) Financial systems: ecology and economics: proposing policy by analogy is risky. Nature 469(7330):302–303

    Google Scholar 

  • Jorion P, Zhang G (2009) Credit contagion from counterparty risk. J Finance 64(5):2053–2087

    Google Scholar 

  • Kambhu J, Weidman S, Krishnan N (2007) Part 1: introduction. Econ Policy Rev 13(November):3–14

    Google Scholar 

  • Kashyap AN, Stein J (2000) What do a million observations say about the transmission of monetary policy? Am Econ Rev 90(3):407–428

    Google Scholar 

  • Kaushik R, Battiston S (2013) Credit default swaps drawup networks: too interconnected to be stable? PloS ONE 8(7):e61815

    Google Scholar 

  • King A, Liechty JC, Rossi C, Taylor C (2010) Frameworks for systemic risk monitoring: conference report. Conference report June 2010, The pew financial reform project

  • Klimek P, Poledna S, Farmer JD, Thurner S (2015) To bail-out or to bail-in? Answers from an agent-based model. J Econ Dyn Control 50:144–154

    Google Scholar 

  • Labonte M (2010) The Dodd-Frank Wall street reform and consumer protection act: systemic risk and the federal reserve. Report R41384, congressional research service

  • Leitner Y (2005) Financial networks: contagion, commitment, and private sector bailouts. J Finance 60(6):2925–2953

    Google Scholar 

  • Lenzu S, Tedeschi G (2012) Systemic risk on different interbank network topologies. Phys A 391(18):4331–4341

    Google Scholar 

  • Levy-Carciente S, Kenett DY, Avakian A, Stanley HE, Havlin S (2015) Dynamic macroprudential stress testing using network theory. J Bank Finance 59:164–181

    Google Scholar 

  • Liebowitz SJ, Margolis SE (1994) Network externality: an uncommon tragedy. J Econ Perspect 8(2):133–150

    Google Scholar 

  • Lo AW (2009) Regulatory reform in the wake of the financial crisis of 2007–2008. J Financ Econ Policy 1(1):4–43

    Google Scholar 

  • Madhavan A (2012) Exchange-traded funds, market structure, and the “flash crash”. Financ Anal J 68(4):20–35

    Google Scholar 

  • Markose SM, Giansante S, Gatkowski M, Shaghaghi AR (2010) Too interconnected to fail: financial contagion and systemic risk in network model of cds and other credit enhancement obligations of US Banks. COMISEF working paper WPS-033, computational optimization methods in statistics, econometrics and finance, Giessen, Germany

  • Martínez-Jaramillo S, Pérez OP, Embriz FA, Dey FLG (2010) Systemic risk, financial contagion and financial fragility. J Econ Dyn Control 34(11):2358–2374

    Google Scholar 

  • Mastromatteo I, Zarinelli E, Marsili M (2012) Reconstruction of financial networks for robust estimation of systemic risk. arXiv:1109.6210v2

  • May RM, Levin SA, Sugihara G (2008) Ecology for bankers. Nature 451(21):893–895

    Google Scholar 

  • Mistrulli PE (2011) Assessing financial contagion in the interbank market: maximum entropy versus observed interbank lending patterns. J Bank Finance 35(5):1114–1127

    Google Scholar 

  • Montagna M, Kok C (2013) Multi-layered interbank model for assessing systemic risk. Kiel working paper No. 1873

  • Müller J (2006) Interbank credit lines as a channel of contagion. J Financ Serv Res 29(1):37–60

    Google Scholar 

  • Nier E, Yang J, Yorulmazer T, Alentorn A (2007) Network models and financial stability. J Econ Dyn Control 31(6):2033–2060

    Google Scholar 

  • Papademos L (2009) Financial stability and macro-prudential supervision: objectives, instruments and the role of the ECB, speech. CFS conference “The ECB and its watchers XI”, Frankfurt, Germany, 4 Sept 2009

  • Pegoraro S (2012) Financial fragility and contagion in interbank networks. http://www.ssrn.com/abstract=2246353

  • Pokutta S, Schmaltz C, Stiller S (2011) Measuring systemic risk and contagion in financial networks. SSRN working paper 1773089

  • Poledna S, Molina-Borboa JL, van der Leij M, Martinez-Jaramillo S, Thurner S (2015) Multi-layer network nature of systemic risk in financial networks and its implications. J Financ Stab 20:70–81

    Google Scholar 

  • Puliga M, Caldarelli G, Battiston S (2014) Credit default swaps networks and systemic risk. Sci Rep 4(6822):1–8

    Google Scholar 

  • Riccetti L, Russo A, Gallegati M (2013) Leveraged network-based financial accelerator. J Econ Dyn Control 37(8):1626–1640

    Google Scholar 

  • Roukny T, Bersini H, Pirotte H, Caldarelli G, Battiston S (2013) Default cascades in complex networks: topology and systemic risk. Sci Rep 3(2759):1–8

    Google Scholar 

  • Schweitzer F, Fagiolo G, Sornette D, Vega-Redondo F, Vespignani A, White DR (2009) Economic networks: the new challenges. Science 325(5939):422–425

    Google Scholar 

  • Segoviano MA, Goodhart CAE (2009) Banking stability measures. IMF working paper 09/04, International Monetary Fund, Washington, DC

  • Shleifer A, Vishny R (2010) Unstable banking. J Financ Econ 97:306–318

    Google Scholar 

  • Shleifer A, Vishny R (2011) Fire sales in finance and macroeconomics. J Econ Perspect 25(1):29–48

    Google Scholar 

  • Sieczka P, Sornette D, Holyst JA (2011) The Lehman Brothers effect and bankruptcy cascades. Eur Phys J B 82(3–4):257–269

    Google Scholar 

  • Soramäki K, Bech ML, Arnold J, Glass RJ, Beyeler WE (2007) The topology of interbank payment flows. Phys A 379(1):317–333

    Google Scholar 

  • Sordi S, Vercelli A (2012) Heterogeneous expectations and strong uncertainty in a minskyian model of financial fluctuations. J Econ Behav Org 83(3):544–557

    Google Scholar 

  • Squartini T, van Lelyveld I, Garlaschelli D (2013) Early-warning signals of topological collapse in interbank networks. Sci Rep 3(3357):1–9

    Google Scholar 

  • Stiglitz JE (2010) Risk and global economic architecture: why full financial integration may be undesirable. Am Econ Rev Pap Proc 100(May):388–392

    Google Scholar 

  • Taleb NN (2010) The black swan: the impact of the highly improbable, trade, Paperback edn. Random House, New York

    Google Scholar 

  • Taleb NN (2011) Antifragility, robustness, and fragility inside the ’Black Swan Domain’. SSRN working paper 1669317

  • Tedeschi G, Mazloumian A, Gallegati M, Helbing D (2012) Bankruptcy cascades in interbank markets. PLoS ONE 7(12):1–10

    Google Scholar 

  • Teteryatnikova M (2014) Systemic risk in banking networks: advantages of “tiered” banking systems. J Econ Dyn Control 47:186–210

    Google Scholar 

  • Thurner S (2011) Systemic financial risk: agent-based models to understand the leverage cycle on national scales and its consequences. January, OECD International Futures Programme

  • Thurner S, Poledna S (2013) DebtRank-transparency: controlling systemic risk in financial networks. Sci Rep 3(1888):1–7

    Google Scholar 

  • Turner A (2011) Leverage, maturity transformation and financial stability: challenges beyond Basel III, speech. Speech given to Cass business school, 16 March 2011. http://www.mondovisione.com/_assets/files/FSA031611_at.pdf. Accessed 29 Oct 2016

  • Upper C, Worms A (2004) Estimating bilateral exposures in the german interbank market: is there a danger of contagion? Eur Econ Rev 48(4):827–849

    Google Scholar 

  • Upper C (2011) Simulation methods to assess the danger of contagion in interbank markets. J Financ Stab 7(3):111–125

    Google Scholar 

  • Yellen J (2013) Interconnectedness and systemic risk: lessons from the financial crisis and policy implications, speech. Speech Given to the American Economic Association, 4 Jan 2013. http://www.federalreserve.gov/newsevents/speech/Yellen20130104a.pdf. Accessed 29 Oct 2016

  • Zhou C (2010) Are banks too big to fail? Measuring systemic importance of financial institutions. Int J Cent Bank 6(4):205–250

    Google Scholar 

  • Zhou C (2013) The impact of imposing capital requirements on systemic risk. J Financ Stab 9(3):320–329

    Google Scholar 

  • Zigrand J (2010) What do Network Theory and Endogenous Risk Theory Have to Say About the Effects of Central Counterparties on Systemic Stability? Banque de France, Financial Stability Review 14:153–160

    Google Scholar 

Download references

Acknowledgments

The author would like to thank Ehsan Ahmed and German Creamer for helpful comments on previous drafts. Several comments by anonymous reviewers have also helped to improve the paper in many ways. The author would also like to thank Adam Diehl for his helpful research assistance. Finally, the author would also like to acknowledge comments from participants at the Agent-based Computational Economics sessions sponsored by the NYC Computational Economics & Complexity Workshop and participants in the Southern Economic Association session on Complexity in Economics and the Social Sciences.

Author information

Authors and Affiliations

  1. James Madison University, MSC 0204, Harrisonburg, VA, 22807, USA

    Andre R. Neveu

Authors
  1. Andre R. Neveu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andre R. Neveu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Neveu, A.R. A survey of network-based analysis and systemic risk measurement. J Econ Interact Coord 13, 241–281 (2018). https://doi.org/10.1007/s11403-016-0182-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11403-016-0182-z

Keywords

JEL Classification

Search

Navigation