On the Performance of Recovery Rate Modeling

  • J. Samuel Baixauli
  • Susana Alvarez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3982)


To ensure accurate predictions of loss given default it is necessary to test the goodness-of-fit of the recovery rate data to the Beta distribution, assuming that its parameters are unknown. In the presence of unknown parameters, the Cramer-von Mises test statistic is neither asymptotically distribution free nor parameter free. In this paper, we propose to compute approximated critical values with a parametric bootstrap procedure. Some simulations show that the bootstrap procedure works well in practice.


Recovery Rate Asymptotic Distribution Credit Risk Beta Distribution Null Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Babu, G.J., Rao, C.R.: Goodness-of-fit Tests when Parameters Are Estimated. Sankhya: Indian J. Statist. 66, 63–74 (2004)MATHMathSciNetGoogle Scholar
  2. 2.
    Carty, L.V., Lieberman, D.: Corporate Bond Defaults and Default Rates. Moody’s Investor Service (1996)Google Scholar
  3. 3.
    Darling, D.A.: The Cramer-Smirnov Test in the Parametric Case. Ann. Math. Statist. 26, 1–20 (1955)MATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Durbin, J.: Weak Convergence of the Sample Distribution Function when Parameters Are Estimated. Ann. Statist. 1, 279–290 (1973)MATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Gordy, M., Jones, D.: Capital Allocation for Securitizations with Uncertaintly in Loss Prioritization. Federal Reserve Board (2002)Google Scholar
  6. 6.
    Gupton, G.M., Stein, R.M.: LossCalcTM: Model for Predicting Loss Given Default (LGD). Moody’s Investors Service (2002)Google Scholar
  7. 7.
    Gupton, G.M., Stein, R.M.: LossCalcv2: Dynamic Prediction of LGD. Moody’s Investors Service (2005)Google Scholar
  8. 8.
    Kac, M., Kiefer, J., Wolfowitz, J.: On tests of Normality and other Tests of Goodness-of-fit, Based on Distance Method. Ann. Math. Statist. 26, 189–211 (1955)MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Pesaran, M.H., Schuermann, T., Treutler, B.J., Weiner, S.M.: Macroeconomic Dynamics and Credit Risk: a Global Perspective. DAE Working Paper No 0330. University of Cambridge (2003)Google Scholar
  10. 10.
    Romano, J.P.: A Bootstrap Revival of some Nonparametric Distance Tests. J. Amer. Statist. Assoc. 83, 698–708 (1988)MATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Stephens, M.A.: Asymptotic Results for Goodness-of-fit Statistics with Unknown Parameters. Ann. Statist. 4, 357–369 (1976)MATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Sukhatme, S.: Fredholm Determinant of a Positive Definite Kernel of a Special Type and its Application. Ann. Math. Statist. 43, 1914–1926 (1972)MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • J. Samuel Baixauli
    • 1
  • Susana Alvarez
    • 2
  1. 1.Department of Management and FinanceUniversity of MurciaSpain
  2. 2.Department of Quantitative Methods for the EconomyUniversity of MurciaSpain

Personalised recommendations