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Financial Network Systemic Risk Contributions - Hautsch, Schaumburg, Schienle - 14-16 December 2013

SYRTO Project
SYRTO Project

Financial Network Systemic Risk Contributions - Hautsch, Schaumburg, Schienle - 14-16 December 2013 - CFE, 7th International Conference on Computational and Financial Econometrics

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Financial Network Systemic Risk Contributions SYstemic Risk TOmography: Signals, Measurements, Transmission Channels, and Policy Interventions Nikolaus Hautsch - University of Vienna Julia Schaumburg - VU University Amsterdam, Tinbergen Institute Melanie Schienle - Leibniz Universit辰t Hannover ! CFE - 7th International Conference on Computational and Financial Econometrics London, December 14-16, 2013
Financial Network Systemic Risk Contributions Nikolaus Hautsch University of Vienna Julia Schaumburg VU University Amsterdam, Tinbergen Institute Melanie Schienle Leibniz Universit即at Hannover
Introduction 2 Systemic Risk Systemic risk: Breakdown risk of the 鍖nancial system induced by the interdependence of its constituents. In crisis times, banks face liquidity shortage, undercapitalisation; leading to 鍖re-sales, hoarding; further enhanced by pro-cyclicality of capital requirements. Consequences of interdependence: Spillovers of risks Co-movements of losses = Systemic risk network Financial Network Systemic Risk Contributions
Introduction 3 Network-based systemic risk assessment Before the 鍖nancial crisis 20072009, systemic risk was neglected by regulation authorities. Hellwig (2009), p. 134: Regulatory reform must [...] address the risks generated by [...] interdependence and by the lack of transparency about systemic risk exposure. Need for a transparent measure for systemic risk that takes interdependence (risk spillovers) into account. Financial Network Systemic Risk Contributions
Introduction 4 Here: VaR-Based Systemic Risk Contributions Objective: Individual banks contributions to system tail risk: stress test-type analysis given publicly available data Time-varying Value at Risk (VaR) conditional on observations of V Pr(Xt qp,t(Xt)) = Pr(Xt VaRp,t) = p, where qp,t(Xt) = qp(Xt|V = Vt) is the pth cond. return quantile. Estimation of a time-varying reduced form relation in quantiles VaRs q,t = gt(VaRi p,t) = g(VaRi p,t, Bt) given control variables Bt and VaRi p,t = VaRi p(Wt). Time-varying systemic risk contribution VaRs q,t VaRi p,t = gt(.) VaRi p,t =: 硫 s|i t Financial Network Systemic Risk Contributions
Introduction 5 Our approach Step 0 Selection of relevant tail risk drivers for each 鍖rm i: LASSO for quantiles other companies tail risk macro environment individual characteristics Step 1 Estimation of VaRi : post-LASSO quantile regression Step 2 Measuring each is time-varying contribution to system risk: VaRs as function of VaRi control variables: selected companies VaRj s (from Step 0), macro environment test of signi鍖cance of VaRi for VaRs Financial Network Systemic Risk Contributions
Introduction 6 Contribution Identi鍖cation of tail risk cross-linkages between 鍖nancial companies (Systemic risk network) Estimation of and inference for two-stage quantile regression model Identi鍖cation of systemically relevant companies and quanti鍖cation of systemic risk contributions Time-varying systemic risk rankings Financial Network Systemic Risk Contributions
Network 7 U.S. network of risk spillovers Financial Network Systemic Risk Contributions
Systemic risk betas 8 Systemic risk ranking during the crisis (June 08) Rank Name 硫 s|i 2008 揃 102 硫 s|i 2008 VaR i 2008 1 BANK OF AMERICA 2.86 0.186 0.154 2 AMERICAN EXPRESS 2.78 0.278 0.100 3 WELLS FARGO & CO 2.51 0.186 0.135 4 MARSHALL & ILSLEY 2.31 0.516 0.045 5 JP MORGAN CHASE & CO. 2.22 0.265 0.084 6 PROGRESSIVE OHIO 1.97 0.380 0.052 7 LEGG MASON 1.96 0.137 0.143 8 REGIONS FINANCIAL 1.86 0.107 0.173 9 MARSH & MCLENNAN 1.76 0.471 0.037 10 STATE STREET 1.44 0.171 0.084 11 NY.CMTY.BANC. 1.12 0.090 0.125 12 PNC FINANCIAL SVS. GP 1.09 0.153 0.071 13 CHUBB 1.07 0.176 0.061 14 TORCHMARK 1.00 0.177 0.057 15 CHARLES SCHWAB 0.91 0.149 0.060 16 CITIGROUP 0.90 0.072 0.124 17 MORGAN STANLEY 0.61 0.074 0.083 18 ZIONS BANCORP. 0.58 0.058 0.100 19 UNUM GROUP 0.34 0.033 0.104 20 UNION PACIFIC 0.27 0.047 0.056 21 HARTFORD FINL.SVS.GP. 0.24 0.012 0.201 22 FRANKLIN RESOURCES 0.17 0.026 0.064 23 T ROWE PRICE GP. 0.01 0.001 0.102 Financial Network Systemic Risk Contributions
Case study 9 Pre-crisis case study Excluding the 鍖nancial crisis time period, can we infer companies developments from our method? Repeat entire estimation and testing procedure using only data until June 2007 Focus on companies that were a鍖ected by the crisis: AIG, Freddie Mac, Lehman Brothers, Merrill Lynch Results are reasonable with respect to systemic riskiness and tail risk dependencies. Financial Network Systemic Risk Contributions
Conclusion 10 Conclusions Transparent risk measure accounting for tail risk dependencies. Approach provides complementing qualitative (tail risk network) and quantitative (systemic risk ranking) information on the U.S. 鍖nancial sector. Although direct backtesting is not possible, plausibility checks suggest that the method works. Measure may be extended in several ways. Financial Network Systemic Risk Contributions
This project is funded by the European Union under the 7th Framework Programme (FP7-SSH/2007-2013) Grant Agreement n属320270 ! ! ! ! ! ! ! www.syrtoproject.eu This document reflects only the authors views. The European Union is not liable for any use that may be made of the information contained therein.

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Financial Network Systemic Risk Contributions - Hautsch, Schaumburg, Schienle - 14-16 December 2013

  • 1. Financial Network Systemic Risk Contributions SYstemic Risk TOmography: Signals, Measurements, Transmission Channels, and Policy Interventions Nikolaus Hautsch - University of Vienna Julia Schaumburg - VU University Amsterdam, Tinbergen Institute Melanie Schienle - Leibniz Universit辰t Hannover ! CFE - 7th International Conference on Computational and Financial Econometrics London, December 14-16, 2013
  • 2. Financial Network Systemic Risk Contributions Nikolaus Hautsch University of Vienna Julia Schaumburg VU University Amsterdam, Tinbergen Institute Melanie Schienle Leibniz Universit即at Hannover
  • 3. Introduction 2 Systemic Risk Systemic risk: Breakdown risk of the 鍖nancial system induced by the interdependence of its constituents. In crisis times, banks face liquidity shortage, undercapitalisation; leading to 鍖re-sales, hoarding; further enhanced by pro-cyclicality of capital requirements. Consequences of interdependence: Spillovers of risks Co-movements of losses = Systemic risk network Financial Network Systemic Risk Contributions
  • 4. Introduction 3 Network-based systemic risk assessment Before the 鍖nancial crisis 20072009, systemic risk was neglected by regulation authorities. Hellwig (2009), p. 134: Regulatory reform must [...] address the risks generated by [...] interdependence and by the lack of transparency about systemic risk exposure. Need for a transparent measure for systemic risk that takes interdependence (risk spillovers) into account. Financial Network Systemic Risk Contributions
  • 5. Introduction 4 Here: VaR-Based Systemic Risk Contributions Objective: Individual banks contributions to system tail risk: stress test-type analysis given publicly available data Time-varying Value at Risk (VaR) conditional on observations of V Pr(Xt qp,t(Xt)) = Pr(Xt VaRp,t) = p, where qp,t(Xt) = qp(Xt|V = Vt) is the pth cond. return quantile. Estimation of a time-varying reduced form relation in quantiles VaRs q,t = gt(VaRi p,t) = g(VaRi p,t, Bt) given control variables Bt and VaRi p,t = VaRi p(Wt). Time-varying systemic risk contribution VaRs q,t VaRi p,t = gt(.) VaRi p,t =: 硫 s|i t Financial Network Systemic Risk Contributions
  • 6. Introduction 5 Our approach Step 0 Selection of relevant tail risk drivers for each 鍖rm i: LASSO for quantiles other companies tail risk macro environment individual characteristics Step 1 Estimation of VaRi : post-LASSO quantile regression Step 2 Measuring each is time-varying contribution to system risk: VaRs as function of VaRi control variables: selected companies VaRj s (from Step 0), macro environment test of signi鍖cance of VaRi for VaRs Financial Network Systemic Risk Contributions
  • 7. Introduction 6 Contribution Identi鍖cation of tail risk cross-linkages between 鍖nancial companies (Systemic risk network) Estimation of and inference for two-stage quantile regression model Identi鍖cation of systemically relevant companies and quanti鍖cation of systemic risk contributions Time-varying systemic risk rankings Financial Network Systemic Risk Contributions
  • 8. Network 7 U.S. network of risk spillovers Financial Network Systemic Risk Contributions
  • 9. Systemic risk betas 8 Systemic risk ranking during the crisis (June 08) Rank Name 硫 s|i 2008 揃 102 硫 s|i 2008 VaR i 2008 1 BANK OF AMERICA 2.86 0.186 0.154 2 AMERICAN EXPRESS 2.78 0.278 0.100 3 WELLS FARGO & CO 2.51 0.186 0.135 4 MARSHALL & ILSLEY 2.31 0.516 0.045 5 JP MORGAN CHASE & CO. 2.22 0.265 0.084 6 PROGRESSIVE OHIO 1.97 0.380 0.052 7 LEGG MASON 1.96 0.137 0.143 8 REGIONS FINANCIAL 1.86 0.107 0.173 9 MARSH & MCLENNAN 1.76 0.471 0.037 10 STATE STREET 1.44 0.171 0.084 11 NY.CMTY.BANC. 1.12 0.090 0.125 12 PNC FINANCIAL SVS. GP 1.09 0.153 0.071 13 CHUBB 1.07 0.176 0.061 14 TORCHMARK 1.00 0.177 0.057 15 CHARLES SCHWAB 0.91 0.149 0.060 16 CITIGROUP 0.90 0.072 0.124 17 MORGAN STANLEY 0.61 0.074 0.083 18 ZIONS BANCORP. 0.58 0.058 0.100 19 UNUM GROUP 0.34 0.033 0.104 20 UNION PACIFIC 0.27 0.047 0.056 21 HARTFORD FINL.SVS.GP. 0.24 0.012 0.201 22 FRANKLIN RESOURCES 0.17 0.026 0.064 23 T ROWE PRICE GP. 0.01 0.001 0.102 Financial Network Systemic Risk Contributions
  • 10. Case study 9 Pre-crisis case study Excluding the 鍖nancial crisis time period, can we infer companies developments from our method? Repeat entire estimation and testing procedure using only data until June 2007 Focus on companies that were a鍖ected by the crisis: AIG, Freddie Mac, Lehman Brothers, Merrill Lynch Results are reasonable with respect to systemic riskiness and tail risk dependencies. Financial Network Systemic Risk Contributions
  • 11. Conclusion 10 Conclusions Transparent risk measure accounting for tail risk dependencies. Approach provides complementing qualitative (tail risk network) and quantitative (systemic risk ranking) information on the U.S. 鍖nancial sector. Although direct backtesting is not possible, plausibility checks suggest that the method works. Measure may be extended in several ways. Financial Network Systemic Risk Contributions
  • 12. This project is funded by the European Union under the 7th Framework Programme (FP7-SSH/2007-2013) Grant Agreement n属320270 ! ! ! ! ! ! ! www.syrtoproject.eu This document reflects only the authors views. The European Union is not liable for any use that may be made of the information contained therein.