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Recovery Rates, Default Probabilities and the Credit Cycle

  • Carlos González-Aguado
  • Max Bruche

Recovery rates are negatively related to default probabilities (Altman et al.,2005). This paper proposes and estimates a model in which this dependence is the result of an unobserved credit cycle: When times are bad, the default probability is high and recovery rates are low; when times are good, the default probability is low and recovery rates are high. The proposed dynamic model is shown to produce a better fit to the data than a standard static approach. It indicates that ignoring the dynamic nature of credit risk could lead to a severe underestimation of credit risk (e.g. by a factor of up to 1.7 in terms of the 95% VaR). 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 Financial Markets Group in its series FMG Discussion Papers with number dp572.

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Date of creation: Nov 2006
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Handle: RePEc:fmg:fmgdps:dp572
Contact details of provider: Web page: http://www.lse.ac.uk/fmg/

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  1. Edward I. Altman & Brooks Brady & Andrea Resti & Andrea Sironi, 2005. "The Link between Default and Recovery Rates: Theory, Empirical Evidence, and Implications," The Journal of Business, University of Chicago Press, vol. 78(6), pages 2203-2228, November.
  2. Nickell, Pamela & Perraudin, William & Varotto, Simone, 2000. "Stability of rating transitions," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 203-227, January.
  3. Bruce E. Hansen, 1995. "Erratum: The Likelihood ratio Test Under Nonstandard Conditions: Testing the Markov Switching Model of GNP," Boston College Working Papers in Economics 296., Boston College Department of Economics.
  4. Bonfim, Diana, 2009. "Credit risk drivers: Evaluating the contribution of firm level information and of macroeconomic dynamics," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 281-299, February.
  5. 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.
  6. Sudheer Chava & Catalina Stefanescu & Stuart Turnbull, 2011. "Modeling the Loss Distribution," Management Science, INFORMS, vol. 57(7), pages 1267-1287, July.
  7. 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.
  8. 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.
  9. Jarrow, Robert A & Turnbull, Stuart M, 1995. " Pricing Derivatives on Financial Securities Subject to Credit Risk," Journal of Finance, American Finance Association, vol. 50(1), pages 53-85, March.
  10. 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.
  11. 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.
  12. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-83, November.
  13. Jon Frye, 2000. "Depressing recoveries," Emerging Issues, Federal Reserve Bank of Chicago, issue Oct.
  14. Long Chen & Pierre Collin-Dufresne & Robert S. Goldstein, 2009. "On the Relation Between the Credit Spread Puzzle and the Equity Premium Puzzle," Review of Financial Studies, Society for Financial Studies, vol. 22(9), pages 3367-3409, September.
  15. 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.
  16. Giacomo Giampieri & Mark Davis & Martin Crowder, 2005. "Analysis of default data using hidden Markov models," Quantitative Finance, Taylor & Francis Journals, vol. 5(1), pages 27-34.
  17. Grunert, Jens & Weber, Martin, 2009. "Recovery rates of commercial lending: Empirical evidence for German companies," Journal of Banking & Finance, Elsevier, vol. 33(3), pages 505-513, March.
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