A Survey of Systemic Risk Analytics
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OFFICE OF FINANCIAL RESEARCH U.S. DEPARTMENT OF THE TREASURY Office of Financial Research Working Paper #0001 January 5, 2012 A Survey of Systemic Risk Analytics 1 Dimitrios Bisias 2 Mark Flood 3 Andrew W. Lo 4 Stavros Valavanis 1 MIT Operations Research Center 2 Senior Policy Advisor, OFR, [email protected] 3 MIT Sloan School of Management, [email protected] 4 MIT Laboratory for Financial Engineering The Office of Financial Research (OFR) Working Paper Series allows staff and their co-authors to disseminate preliminary research findings in a format intended to generate discussion and critical comments. Papers in the OFR Working Paper Series are works in progress and subject to revision. Views and opinions expressed are those of the authors and do not necessarily represent official OFR or Treasury positions or policy. Comments are welcome as are suggestions for improvements, and should be directed to the authors. OFR Working Papers may be quoted without additional permission. www.treasury.gov/ofr A Survey of Systemic Risk Analytics∗ Dimitrios Bisias†, Mark Flood‡, Andrew W. Lo§, Stavros Valavanis¶ This Draft: January 5, 2012 We provide a survey of 31 quantitative measures of systemic risk in the economics and finance literature, chosen to span key themes and issues in systemic risk measurement and manage- ment. We motivate these measures from the supervisory, research, and data perspectives in the main text, and present concise definitions of each risk measure—including required inputs, expected outputs, and data requirements—in an extensive appendix. To encourage experimentation and innovation among as broad an audience as possible, we have developed open-source Matlab code for most of the analytics surveyed, which can be accessed through the Office of Financial Research (OFR) at http://www.treasury.gov/ofr. Keywords: Systemic Risk; Financial Institutions; Liquidity; Financial Crises; Risk Man- agement JEL Classification: G12, G29, C51 ∗We thank Tobias Adrian, Lewis Alexander, Dick Berner, Markus Brunnermeier, Jayna Cummings, Dar- rell Duffie, Doyne Farmer, Michael Gibson, Jeff King, Nellie Lang, Adam LaVier, Bob Merton, Bill Nichols, Wayne Passmore, Patrick Pinschmidt, John Schindler, Jonathan Sokobin, Hao Zhou, and participants at the 2011 OFR/FSOC Macroprudential Toolkit Conference for helpful comments and discussion, and Alex Wang for excellent research assistance. Research support from the Office of Financial Research is grate- fully acknowledged. The views and opinions expressed in this article are those of the authors only, and do not necessarily represent the views and opinions of AlphaSimplex Group, MIT, any of their affiliates and employees, or any of the individuals acknowledged above. †MIT Operations Research Center. ‡Office of Financial Research. §MIT Sloan School of Management, MIT Laboratory for Financial Engineering, and AlphaSimplex Group, LLC. ¶MIT Laboratory for Financial Engineering. Contents 1 Introduction 1 2 Supervisory Perspective 6 2.1 TrendsintheFinancialSystem ......................... 7 2.2 Policy Applications . 10 2.3 The Lucas Critique and Systemic Risk Supervision . 14 2.4 SupervisoryTaxonomy .............................. 15 2.5 Event/DecisionHorizonTaxonomy . 21 3 Research Perspective 27 3.1 Conceptual Framework and Econometric Issues . ..... 27 3.2 Nonstationarity .................................. 29 3.3 OtherResearchDirections . .. .. 30 3.4 ResearchTaxonomy................................ 33 4 Data Issues 38 4.1 DataRequirements ................................ 39 4.2 LegalEntityIdentifierStandards . .. 39 4.3 Privacyvs.Transparency. 45 5 Conclusions 46 Appendix 48 A Macroeconomic Measures 48 A.1 Costly Asset-Price Boom/Bust Cycles . .. 49 A.2 Property-Price, Equity-Price, and Credit-Gap Indicators . ........ 53 A.3 MacroprudentialRegulation . 55 B Granular Foundations and Network Measures 57 B.1 TheDefaultIntensityModel . 60 B.2 Network Analysis and Systemic Financial Linkages . .. 63 B.3 Simulating a Credit Scenario . 63 B.4 Simulating a Credit-and-Funding-Shock Scenario . .... 64 B.5 Granger-CausalityNetworks . 65 B.6 Bank Funding Risk and Shock Transmission . 69 B.7 Mark-to-Market Accounting and Liquidity Pricing . ... 73 C Forward-Looking Risk Measurement 74 C.1 Contingent Claims Analysis . 77 C.2 MahalanobisDistance .............................. 80 C.3 TheOptioniPoD ................................. 81 C.4 Multivariate Density Estimators . 85 C.5 Simulating the Housing Sector . 89 C.6 ConsumerCredit ................................. 94 i C.7 Principal Components Analysis . 98 D Stress Tests 100 D.1 GDPStressTests................................. 100 D.2 LessonsfromtheSCAP ............................. 102 D.3 A10-by-10-by-10Approach . 102 E Cross-Sectional Measures 104 E.1 CoVaR....................................... 105 E.2 DistressedInsurancePremium . 108 E.3 Co-Risk ...................................... 112 E.4 MarginalandSystemicExpectedShortfall . 114 F Measures of Illiquidity and Insolvency 116 F.1 RiskTopography ................................. 120 F.2 TheLeverageCycle................................ 122 F.3 Noise as Information for Illiquidity . 123 F.4 Crowded Trades in Currency Funds . 125 F.5 Equity Market Illiquidity . 127 F.6 Serial Correlation and Illiquidity in Hedge Fund Returns . 129 F.7 Broader Hedge-Fund-Based Systemic Risk Measures . ...... 133 G Matlab Program Headers 137 G.1 Function delta co var .............................. 137 G.2 Function quantile regression ......................... 138 G.3 Function co risk ................................. 138 G.4 Function turbulence ............................... 138 G.5 Function turbulence var ............................ 139 G.6 Function marginal expected shortfall .................... 139 G.7 Function leverage ................................ 139 G.8 Function systemic expected shortfall .................... 139 G.9 Function contrarian trading strategy .................... 140 G.10 Function kyles lambda .............................. 140 G.11 Function systemic liquidity indicator ................... 140 G.12 Function probability liquidation model .................. 141 G.13 Function crowded trades ............................ 141 G.14 Function cca ................................... 141 G.15 Function distressed insurance premium ................... 142 G.16 Function credit funding shock ........................ 142 G.17 Function pca ................................... 143 G.18 Function linear granger causality ...................... 143 G.19 Function hac regression ............................ 143 G.20 Function dynamic causality index ...................... 144 G.21 Function dijkstra ................................ 144 G.22 Function calc closeness ............................ 145 G.23 Function network measures ........................... 145 ii G.24 Function absorption ratio ........................... 146 G.25 Function dar ................................... 146 G.26 Function undirected banking linkages .................... 146 G.27 Function directed banking linkages ..................... 146 G.28 Function optimal gap thresholds ....................... 147 G.29 Function joint gap indicators ........................ 147 G.30 Function stress scenario selection ..................... 148 G.31 Function fit ar model .............................. 148 G.32 Function systemic risk exposures ...................... 148 iii 1 Introduction In July 2010, the U.S. Congress enacted the Dodd Frank Wall Street Reform and Consumer Protection Act (Dodd Frank Act), the most comprehensive financial reform bill since the 1930s. Among other things, the Dodd Frank Act created the Financial Stability Oversight Council (FSOC) and Office of Financial Research (OFR). The FSOC has three broad man- dates: (1) to identify risks to financial stability arising from events or activities of large financial firms or elsewhere; (2) to promote market discipline by eliminating participants’ expectations of possible government bailouts; and (3) to respond to emerging threats to the stability of the financial system.1 The starting point for all of these directives is the accurate and timely measurement of systemic risk. The truism that “one cannot manage what one does not measure” is especially compelling for financial stability since policymakers, regula- tors, academics, and practitioners have yet to reach a consensus on how to define “systemic risk”. While regulators sometimes apply Justice Potter Stewart’s definition of pornography, i.e., systemic risk may be hard to define but they know it when they see it, such a vague and subjective approach is not particularly useful for measurement and analysis, a pre-requisite for addressing threats to financial stability. One definition of systemic risk is “any set of circumstances that threatens the stability of or public confidence in the financial system” (Billio, Getmansky, Lo, and Pelizzon, 2010). The European Central Bank (ECB) (2010) defines it as a risk of financial instability “so widespread that it impairs the functioning of a financial system to the point where economic growth and welfare suffer materially”. Others have focused on more specific mechanisms, including imbalances (Caballero, 2009), correlated exposures (Acharya, Pedersen, Philippon, and Richardson, 2010), spillovers to the real economy (Group of Ten, 2001), information disruptions (Mishkin, 2007), feedback behavior (Kapadia, Drehmann, Elliott, and Sterne, 2009), asset bubbles (Rosengren, 2010), contagion (Moussa, 2011), and negative externalities (Financial Stability