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Modeling the Loss Distribution

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Author Info

  • Sudheer Chava

    ()
    (College of Management, Georgia Institute of Technology, Atlanta, Georgia 30308)

  • Catalina Stefanescu

    ()
    (ESMT European School of Management and Technology, 10178 Berlin, Germany)

  • Stuart Turnbull

    ()
    (Bauer College of Business, University of Houston, Houston, Texas 77204)

Abstract

In this paper, we focus on modeling and predicting the loss distribution for credit risky assets such as bonds and loans. We model the probability of default and the recovery rate given default based on shared covariates. We develop a new class of default models that explicitly accounts for sector specific and regime dependent unobservable heterogeneity in firm characteristics. Based on the analysis of a large default and recovery data set over the horizon 1980-2008, we document that the specification of the default model has a major impact on the predicted loss distribution, whereas the specification of the recovery model is less important. In particular, we find evidence that industry factors and regime dynamics affect the performance of default models, implying that the appropriate choice of default models for loss prediction will depend on the credit cycle and on portfolio characteristics. Finally, we show that default probabilities and recovery rates predicted out of sample are negatively correlated and that the magnitude of the correlation varies with seniority class, industry, and credit cycle. This paper was accepted by Wei Xiong, finance.

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File URL: http://dx.doi.org/10.1287/mnsc.1110.1345
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Bibliographic Info

Article provided by INFORMS in its journal Management Science.

Volume (Year): 57 (2011)
Issue (Month): 7 (July)
Pages: 1267-1287

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Handle: RePEc:inm:ormnsc:v:57:y:2011:i:7:p:1267-1287

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Related research

Keywords: loss distribution; default prediction; recovery rates; Basel II;

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Cited by:
  1. Xu, Xin, 2013. "Forecasting Bankruptcy with Incomplete Information," MPRA Paper 55024, University Library of Munich, Germany, revised 31 Mar 2014.
  2. Manasa Gopal & Markus Pasche, 2012. "Market-based Eurobonds Without Cross-Subsidisation," Global Financial Markets Working Paper Series 2012-37, Friedrich-Schiller-University Jena.
  3. Jiří Witzany & Michal Rychnovský & Pavel Charamza, 2010. "Survival Analysis in LGD Modeling," Working Papers IES 2010/02, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Feb 2010.
  4. Ruey-Ching Hwang & Huimin Chung & Jiun-Yi Ku, 2013. "Predicting Recurrent Financial Distresses with Autocorrelation Structure: An Empirical Analysis from an Emerging Market," Journal of Financial Services Research, Springer, vol. 43(3), pages 321-341, June.
  5. Alexander Becker & Alexander F. R. Koivusalo & Rudi Sch\"afer, 2012. "Empirical Evidence for the Structural Recovery Model," Papers 1203.3188, arXiv.org.
  6. Schläfer, Timo & Uhrig-Homburg, Marliese, 2014. "Is recovery risk priced?," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 257-270.
  7. Hwang, Ruey-Ching, 2012. "A varying-coefficient default model," International Journal of Forecasting, Elsevier, vol. 28(3), pages 675-688.

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