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Recovery Rates, Default Probabilities And The Credit Cycle

  • Max Bruche

    ()

  • Carlos González Aguado

    ()

    (CEMFI, Centro de Estudios Monetarios y Financieros)

Default probabilities and recovery rate densities are not constant over the credit cycle; yet many models assume that they are. This paper proposes and estimates a model in which these two variables depend on an unobserved credit cycle, modelled by a twostate Markov chain. The proposed model is shown to produce a better fit to observed recoveries than a standard static approach. The model indicates that ignoring the dynamic nature of credit risk could lead to a severe underestimation of e.g. the 95% VaR, such that the actual VaR could be higher by a factor of up to 1.7. Also, the model indicates that the credit cycle is related to but distinct from the business cycle as e.g. determined by the NBER, which might explain why previous studies have found the power of macroeconomic variables in explaining default probabilities and recoveries to be low.

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Paper provided by CEMFI in its series Working Papers with number wp2006_0612.

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Date of creation: Sep 2006
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Handle: RePEc:cmf:wpaper:wp2006_0612
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  1. Pamela Nickell & William Perraudin & Simone Varotto, 2001. "Stability of ratings transitions," Bank of England working papers 133, Bank of England.
  2. Renault, Olivier & Scaillet, Olivier, 2004. "On the way to recovery: A nonparametric bias free estimation of recovery rate densities," Journal of Banking & Finance, Elsevier, vol. 28(12), pages 2915-2931, December.
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  8. Acharya, Viral V. & Bharath, Sreedhar T. & Srinivasan, Anand, 2007. "Does industry-wide distress affect defaulted firms? Evidence from creditor recoveries," Journal of Financial Economics, Elsevier, vol. 85(3), pages 787-821, September.
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  10. Anil Bangia & Francis X. Diebold & Til Schuermann, 2000. "Ratings Migration and the Business Cycle, With Application to Credit Portfolio Stress Testing," Center for Financial Institutions Working Papers 00-26, Wharton School Center for Financial Institutions, University of Pennsylvania.
  11. Hansen, Bruce E, 1996. "Erratum: The Likelihood Ratio Test under Nonstandard Conditions: Testing the Markov Switching Model of GNP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(2), pages 195-98, March-Apr.
  12. Jon Frye, 2000. "Depressing recoveries," Emerging Issues, Federal Reserve Bank of Chicago, issue Oct.
  13. Sudheer Chava & Catalina Stefanescu & Stuart Turnbull, 2011. "Modeling the Loss Distribution," Management Science, INFORMS, vol. 57(7), pages 1267-1287, July.
  14. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-24, January.
  15. Shleifer, Andrei & Vishny, Robert W, 1992. " Liquidation Values and Debt Capacity: A Market Equilibrium Approach," Journal of Finance, American Finance Association, vol. 47(4), pages 1343-66, September.
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  17. Carling, Kenneth & Jacobson, Tor & Linde, Jesper & Roszbach, Kasper, 2007. "Corporate credit risk modeling and the macroeconomy," Journal of Banking & Finance, Elsevier, vol. 31(3), pages 845-868, March.
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