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Time-Varying Systemic Risk: Evidence from a Dynamic Copula Model of CDS Spreads

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  • Dong Hwan Oh
  • Andrew J. Patton

Abstract

This paper proposes a new class of copula-based dynamic models for high dimension conditional distributions, facilitating the estimation of a wide variety of measures of systemic risk. Our proposed models draw on successful ideas from the literature on modeling high dimension covariance matrices and on recent work on models for general time-varying distributions. Our use of copula-based models enable the estimation of the joint model in stages, greatly reducing the computational burden. We use the proposed new models to study a collection of daily credit default swap (CDS) spreads on 100 U.S. firms over the period 2006 to 2012. We find that while the probability of distress for individual firms has greatly reduced since the financial crisis of 2008-09, the joint probability of distress (a measure of systemic risk) is substantially higher now than in the pre-crisis period.

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Bibliographic Info

Paper provided by Duke University, Department of Economics in its series Working Papers with number 13-30.

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Length: 43
Date of creation: 2013
Date of revision:
Handle: RePEc:duk:dukeec:13-30

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Postal: Department of Economics Duke University 213 Social Sciences Building Box 90097 Durham, NC 27708-0097
Phone: (919) 660-1800
Fax: (919) 684-8974
Web page: http://econ.duke.edu/

Related research

Keywords: correlation; tail risk; financial crises; DCC;

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Cited by:
  1. Marco Bazzi & Francisco Blasques & Siem Jan Koopman & and Andre Lucas, 2014. "Time Varying Transition Probabilities for Markov Regime Switching Models," Tinbergen Institute Discussion Papers 14-072/III, Tinbergen Institute.
  2. Francesco Calvori & Drew Creal & Siem Jan Koopman & Andre Lucas, 2014. "Testing for Parameter Instability in Competing Modeling Frameworks," Tinbergen Institute Discussion Papers 14-010/IV/DSF71, Tinbergen Institute.
  3. Francisco Blasques & Siem Jan Koopman & Andr� Lucas, 2014. "Information Theoretic Optimality of Observation Driven Time Series Models," Tinbergen Institute Discussion Papers 14-046/III, Tinbergen Institute.
  4. Pawel Janus & Andr� Lucas & and Anne Opschoor, 2014. "New HEAVY Models for Fat-Tailed Returns and Realized Covariance Kernels," Tinbergen Institute Discussion Papers 14-073/IV, Tinbergen Institute.
  5. Brechmann, Eike C. & Hendrich, Katharina & Czado, Claudia, 2013. "Conditional copula simulation for systemic risk stress testing," Insurance: Mathematics and Economics, Elsevier, vol. 53(3), pages 722-732.
  6. Francisco Blasques & Siem Jan Koopman & Andre Lucas, 2014. "Maximum Likelihood Estimation for Generalized Autoregressive Score Models," Tinbergen Institute Discussion Papers 14-029/III, Tinbergen Institute, revised 19 Apr 2014.

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