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Modeling the Loss Distribution

  • 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)

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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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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