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OFFICE OFFINANCIAL RESEARCHU.S. DEPARTMENT OF THE TREASURY

Office of Financial Research Working Paper #0001

January 5, 2012

A Survey of Systemic Risk Analytics

Dimitrios Bisias1 Mark Flood2

Andrew W. Lo3 Stavros Valavanis4

1 MIT Operations Research Center 2 Senior Policy Advisor, OFR, mark.flood@treasury.gov 3 MIT Sloan School of Management, alo@mit.edu 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

mailto:mark.flood@treasury.govmailto:alo@mit.eduhttp://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 financeliterature, chosen to span key themes and issues in systemic risk measurement and manage-ment. We motivate these measures from the supervisory, research, and data perspectivesin the main text, and present concise definitions of each risk measureincluding requiredinputs, expected outputs, and data requirementsin an extensive appendix. To encourageexperimentation and innovation among as broad an audience as possible, we have developedopen-source Matlab code for most of the analytics surveyed, which can be accessed throughthe 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 atthe 2011 OFR/FSOC Macroprudential Toolkit Conference for helpful comments and discussion, and AlexWang 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 donot necessarily represent the views and opinions of AlphaSimplex Group, MIT, any of their affiliates andemployees, 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 62.1 Trends in the Financial System . . . . . . . . . . . . . . . . . . . . . . . . . 72.2 Policy Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.3 The Lucas Critique and Systemic Risk Supervision . . . . . . . . . . . . . . 142.4 Supervisory Taxonomy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.5 Event/Decision Horizon Taxonomy . . . . . . . . . . . . . . . . . . . . . . . 21

3 Research Perspective 273.1 Conceptual Framework and Econometric Issues . . . . . . . . . . . . . . . . 273.2 Nonstationarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293.3 Other Research Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303.4 Research Taxonomy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

4 Data Issues 384.1 Data Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 394.2 Legal Entity Identifier Standards . . . . . . . . . . . . . . . . . . . . . . . . 394.3 Privacy vs. Transparency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

5 Conclusions 46

Appendix 48

A Macroeconomic Measures 48A.1 Costly Asset-Price Boom/Bust Cycles . . . . . . . . . . . . . . . . . . . . . . 49A.2 Property-Price, Equity-Price, and Credit-Gap Indicators . . . . . . . . . . . 53A.3 Macroprudential Regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

B Granular Foundations and Network Measures 57B.1 The Default Intensity Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 60B.2 Network Analysis and Systemic Financial Linkages . . . . . . . . . . . . . . 63B.3 Simulating a Credit Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . 63B.4 Simulating a Credit-and-Funding-Shock Scenario . . . . . . . . . . . . . . . . 64B.5 Granger-Causality Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . 65B.6 Bank Funding Risk and Shock Transmission . . . . . . . . . . . . . . . . . . 69B.7 Mark-to-Market Accounting and Liquidity Pricing . . . . . . . . . . . . . . . 73

C Forward-Looking Risk Measurement 74C.1 Contingent Claims Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 77C.2 Mahalanobis Distance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80C.3 The Option iPoD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81C.4 Multivariate Density Estimators . . . . . . . . . . . . . . . . . . . . . . . . . 85C.5 Simulating the Housing Sector . . . . . . . . . . . . . . . . . . . . . . . . . . 89C.6 Consumer Credit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

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C.7 Principal Components Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 98

D Stress Tests 100D.1 GDP Stress Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100D.2 Lessons from the SCAP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102D.3 A 10-by-10-by-10 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

E Cross-Sectional Measures 104E.1 CoVaR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105E.2 Distressed Insurance Premium . . . . . . . . . . . . . . . . . . . . . . . . . . 108E.3 Co-Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112E.4 Marginal and Systemic Expected Shortfall . . . . . . . . . . . . . . . . . . . 114

F Measures of Illiquidity and Insolvency 116F.1 Risk Topography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120F.2 The Leverage Cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122F.3 Noise as Information for Illiquidity . . . . . . . . . . . . . . . . . . . . . . . 123F.4 Crowded Trades in Currency Funds . . . . . . . . . . . . . . . . . . . . . . . 125F.5 Equity Market Illiquidity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127F.6 Serial Correlation and Illiquidity in Hedge Fund Returns . . . . . . . . . . . 129F.7 Broader Hedge-Fund-Based Systemic Risk Measures . . . . . . . . . . . . . . 133

G Matlab Program Headers 137G.1 Function delta co var . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137G.2 Function quantile regression . . . . . . . . . . . . . . . . . . . . . . . . . 138G.3 Function co risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138G.4 Function turbulence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138G.5 Function turbulence var . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139G.6 Function marginal expected shortfall . . . . . . . . . . . . . . . . . . . . 139G.7 Function leverage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139G.8 Function systemic expected shortfall . . . . . . . . . . . . . . . . . . . . 139G.9 Function contrarian trading strategy . . . . . . . . . . . . . . . . . . . . 140G.10 Function kyles lambda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140G.11 Function systemic liquidity indicator . . . . . . . . . . . . . . . . . . . 140G.12 Function probability liquidation model . . . . . . . . . . . . . . . . . . 141G.13 Function crowded trades . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141G.14 Function cca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141G.15 Function distressed insurance premium . . . . . . . . . . . . . . . . . . . 142G.16 Function credit funding shock . . . . . . . . . . . . . . . . . . . . . . . . 142G.17 Function pca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143G.18 Function linear granger causality . . . . . . . . . . . . . . . . . . . . . . 143G.19 Function hac regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143G.20 Function dynamic causality index . . . . . . . . . . . . . . . . . . . . . . 144G.21 Function dijkstra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144G.22 Function calc closeness . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145G.23 Function network measures . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

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G.24 Function absorption ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . 146G.25 Function dar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146G.26 Function undirected banking linkages . . . . . . . . . . . . . . . . . . . . 146G.27 Function directed banking linkages . . . . . . . . . . . . . . . . . . . . . 146G.28 Function optimal gap thresholds . . . . . . . . . . . . . . . . . . . . . . . 147G.29 Function joint gap indicators . . . . . . . . . . . . . . . . . . . . . . . . 147G.30 Function stress scenario selection .