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Recovery Rates, Default Probabilities and the Credit Cycle

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Carlos González-Aguado
Max Bruche

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Abstract

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

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This paper has been announced in the following NEP Reports: References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. 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.
  2. 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. [Downloadable!] (restricted)
  3. Edward I. Altman & Brooks Brady & Andrea Resti & Andrea Sironi, 2005. "The Link between Default and Recovery Rates: Theory, Empirical Evidence, and Implications," Journal of Business, University of Chicago Press, vol. 78(6), pages 2203-2228, November. [Downloadable!]
  4. 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. [Downloadable!] (restricted)
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Gábor Vadas, 2007. "Wealth portfolio of Hungarian households – Urban legends and facts," MNB Occasional Papers 2007/68, Magyar Nemzeti Bank (The Central Bank of Hungary). [Downloadable!]
  2. Balázs Zsámboki, 2007. "Basel II and financial stability: An investigation of sensitivity and cyclicality of capital requirements based on QIS 5," MNB Occasional Papers 2007/67, Magyar Nemzeti Bank (The Central Bank of Hungary). [Downloadable!]
  3. Andrea Cipollini & Giuseppe Missaglia, 2008. "Measuring bank capital requirements through Dynamic Factor analysis," Center for Economic Research (RECent) 010, University of Modena and Reggio E., Dept. of Economics. [Downloadable!]
  4. Gabriel Jiménez & Javier Mencía, 2007. "Modeling the distribution of credit losses with observable and latent factors," Banco de España Working Papers 0709, Banco de España. [Downloadable!]
  5. Ethan Cohen-Cole, 2007. "Asset liquidity, debt valuation and credit risk," Quantitative Analysis Unit Working Paper QAU07-5, Federal Reserve Bank of Boston. [Downloadable!]
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