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Empirical Evidence for the Structural Recovery Model

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  • Alexander Becker
  • Alexander F. R. Koivusalo
  • Rudi Schafer

Abstract

While defaults are rare events, losses can be substantial even for credit portfolios with a large number of contracts. Therefore, not only a good evaluation of the probability of default is crucial, but also the severity of losses needs to be estimated. The recovery rate is often modeled independently with regard to the default probability, whereas the Merton model yields a functional dependence of both variables. We use Moody's Default and Recovery Database in order to investigate the relationship of default probability and recovery rate for senior secured bonds. The assumptions in the Merton model do not seem justified by the empirical situation. Yet the empirical dependence of default probability and recovery rate is well described by the functional dependence found in the Merton model.

Suggested Citation

  • Alexander Becker & Alexander F. R. Koivusalo & Rudi Schafer, 2012. "Empirical Evidence for the Structural Recovery Model," Papers 1203.3188, arXiv.org.
    • Handle: RePEc:arx:papers:1203.3188
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    1. repec:bla:ecnote:v:33:y:2004:i:2:p:183-208 is not listed on IDEAS
    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.
    3. Sudheer Chava & Catalina Stefanescu & Stuart Turnbull, 2011. "Modeling the Loss Distribution," Management Science, INFORMS, vol. 57(7), pages 1267-1287, July.
    4. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
    5. 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.
    6. Rudi Schafer & Alexander F. R. Koivusalo, 2011. "Dependence of defaults and recoveries in structural credit risk models," Papers 1102.3150, arXiv.org, revised Mar 2011.
    7. Sudheer Chava & Robert A. Jarrow, 2008. "Bankruptcy Prediction with Industry Effects," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 21, pages 517-549, World Scientific Publishing Co. Pte. Ltd..
    8. Jon Frye, 2000. "Depressing recoveries," Emerging Issues, Federal Reserve Bank of Chicago, issue Oct.
    9. Benjamin Bade & Daniel Rösch & Harald Scheule, 2011. "Default and Recovery Risk Dependencies in a Simple Credit Risk Model," European Financial Management, European Financial Management Association, vol. 17(1), pages 120-144, January.
    10. Duffie, Darrell & Singleton, Kenneth J, 1999. "Modeling Term Structures of Defaultable Bonds," The Review of Financial Studies, Society for Financial Studies, vol. 12(4), pages 687-720.
    11. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
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    1. repec:ath:journl:tome:34:v:2:y:2014:i:34:p:99-109 is not listed on IDEAS
    2. Schäfer, Rudi & Koivusalo, Alexander F.R., 2013. "Dependence of defaults and recoveries in structural credit risk models," Economic Modelling, Elsevier, vol. 30(C), pages 1-9.
    3. Michael C Münnix & Rudi Schäfer & Thomas Guhr, 2014. "A Random Matrix Approach to Credit Risk," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-9, May.

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