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An empirical analysis of bond recovery rates: exploring a structural view of default

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  • Daniel M. Covitz
  • Song Han

Abstract

A frictionless, structural view of default has the unrealistic implication that recovery rates on bonds, measured at default, should be close to 100 percent. This suggests that standard "frictions" such as default delays, corporate-valuation jumps, and bankruptcy costs may be important drivers of recovery rates. A structural view also suggests the existence of nonlinearities in the empirical relationship between recovery rates and their determinants. We explore these implications empirically and find direct evidence of jumps, and also evidence of the predicted nonlinearities. In particular, recovery rates increase as economic conditions improve from low levels, but decrease as economic conditions become robust. This suggests that improving economic conditions tend to boost firm values, but firms may tend to default during particularly robust times only when they have experienced large, negative shocks.

Suggested Citation

  • Daniel M. Covitz & Song Han, 2004. "An empirical analysis of bond recovery rates: exploring a structural view of default," Finance and Economics Discussion Series 2005-10, Board of Governors of the Federal Reserve System (U.S.).
  • Handle: RePEc:fip:fedgfe:2005-10
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    Citations

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    Cited by:

    1. Khieu, Hinh D. & Mullineaux, Donald J. & Yi, Ha-Chin, 2012. "The determinants of bank loan recovery rates," Journal of Banking & Finance, Elsevier, vol. 36(4), pages 923-933.
    2. Abel Elizalde, 2006. "CREDIT RISK MODELS IV: UNDERSTANDING AND PRICING CDOs," Working Papers wp2006_0608, CEMFI.
    3. 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.
    4. Daniel M. Covitz & Song Han & Beth Anne Wilson, 2006. "Are longer bankruptcies really more costly?," Finance and Economics Discussion Series 2006-27, Board of Governors of the Federal Reserve System (U.S.).
    5. Nada Mora, 2012. "What determines creditor recovery rates?," Economic Review, Federal Reserve Bank of Kansas City, issue Q II.
    6. Mora, Nada, 2015. "Creditor recovery: The macroeconomic dependence of industry equilibrium," Journal of Financial Stability, Elsevier, vol. 18(C), pages 172-186.
    7. Qi, Min & Zhao, Xinlei, 2011. "Comparison of modeling methods for Loss Given Default," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 2842-2855, November.
    8. Sudheer Chava & Catalina Stefanescu & Stuart Turnbull, 2011. "Modeling the Loss Distribution," Management Science, INFORMS, vol. 57(7), pages 1267-1287, July.
    9. Nikola A. Tarashev & Haibin Zhu, 2006. "The pricing of portfolio credit risk," BIS Working Papers 214, Bank for International Settlements.
    10. Cangemi, Robert R. & Mason, Joseph R. & Pagano, Michael S., 2012. "Options-based structural model estimation of bond recovery rates," Journal of Financial Intermediation, Elsevier, vol. 21(3), pages 473-506.
    11. Zhu, Haibin & Tarashev, Nikola A., 2008. "The pricing of correlated default risk: evidence from the credit derivatives market," Discussion Paper Series 2: Banking and Financial Studies 2008,09, Deutsche Bundesbank.

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    Keywords

    Bonds ; Default (Finance) ; Risk management;

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