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Default Recovery Rates in Credit Risk Modelling: A Review of the Literature and Empirical Evidence

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  • Edward Altman
  • Andrea Resti
  • Andrea Sironi

Abstract

Evidence from many countries in recent years suggests that collateral values and recovery rates (RRs) on corporate defaults can be volatile and, moreover, that they tend to go down just when the number of defaults goes up in economic downturns. This link between RRs and default rates has traditionally been neglected by credit risk models, as most of them focused on default risk and adopted static loss assumptions, treating the RR either as a constant parameter or as a stochastic variable independent from the probability of default (PD). This traditional focus on default analysis has been partly reversed by the recent significant increase in the number of studies dedicated to the subject of recovery-rate estimation and the relationship between default and RRs. This paper presents a detailed review of the way credit risk models, developed during the last 30 years, treat the RR and, more specifically, its relationship with the PD of an obligor. Recent empirical evidence concerning this issue is also presented and discussed. Copyright Banca Monte dei Paschi di Siena SpA, 2004

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Article provided by Banca Monte dei Paschi di Siena SpA in its journal Economic Notes.

Volume (Year): 33 (2004)
Issue (Month): 2 (07)
Pages: 183-208
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Handle: RePEc:bla:ecnote:v:33:y:2004:i:2:p:183-208

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Cited by:
  1. Xin Guo & Robert Jarrow & Haizhi Lin, 2008. "Distressed debt prices and recovery rate estimation," Review of Derivatives Research, Springer, vol. 11(3), pages 171-204, October.
  2. Eric Wong & Cho-Hoi Hui, 2009. "A Liquidity Risk Stress-Testing Framework with Interaction between Market and Credit Risks," Working Papers 0906, Hong Kong Monetary Authority.
  3. Claudio, Ferrarese, 2006. "A comparative analysis of correlation skew modeling techniques for CDO index tranches," MPRA Paper 1668, University Library of Munich, Germany.
  4. Jan Willem van den End & Marco Hoeberichts & Mostafa Tabbae, 2006. "Modelling Scenario Analysis and Macro Stress-testing," DNB Working Papers 119, Netherlands Central Bank, Research Department.
  5. Georges Dionne & Geneviève Gauthier & Khemais Hammami & Mathieu Maurice & Jean-Guy Simonato, 2010. "Default Risk in Corporate Yield Spreads," Financial Management, Financial Management Association International, vol. 39(2), pages 707-731, 06.
  6. Yanan Zhang & Lu Ji & Fei Liu, 2010. "Local Housing Market Cycle and Loss Given Default: Evidence from Sub-Prime Residential Mortgages," IMF Working Papers 10/167, International Monetary Fund.
  7. Sosa Navarro, Ramiro, 2005. "Default Recovery Rates and Implied Default Probability Estimations: Evidence from the Argentinean Crisis," MPRA Paper 11054, University Library of Munich, Germany.
  8. Ethan Cohen-Cole, 2007. "Asset liquidity, debt valuation and credit risk," Quantitative Analysis Unit Working Paper QAU07-5, Federal Reserve Bank of Boston.
  9. Alexander Becker & Alexander F. R. Koivusalo & Rudi Sch\"afer, 2012. "Empirical Evidence for the Structural Recovery Model," Quantitative Finance Papers 1203.3188, arXiv.org.
  10. Gürtler, Marc & Heithecker, Dirk, 2005. "Systematic credit cycle risk of financial collaterals: Modelling and evidence," Working Papers FW15V2, Technische Universität Braunschweig, Institute of Finance.
  11. Klaus Rheinberger & Martin Summer, 2008. "Credit portfolio risk and asset price cycles," Computational Management Science, Springer, vol. 5(4), pages 337-354, October.
  12. Gaspar, Raquel M. & Slinko, Irina, 2005. "Correlation Between Intensity and Recovery in Credit Risk Models," Working Paper Series in Economics and Finance 614, Stockholm School of Economics.
  13. Kurt Hess & Arthur Grimes, 2009. "Commercial Bank Loan Loss Recoveries," Working Papers in Economics 09/09, University of Waikato, Department of Economics.

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