Modeling the Loss Distribution

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 s...

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Bibliographic Details
Published in:Management science 2011-07, Vol.57 (7), p.1267-1287
Main Authors: Chava, Sudheer, Stefanescu, Catalina, Turnbull, Stuart
Format: Article
Language:English
Subjects:
Applied sciences
Banking
Basel Accord
Basel II
Bonds
Business losses
Credit
Default
default prediction
Distribution
Dynamic programming
Economic models
Empirical research
Exact sciences and technology
Forecasting models
Frailty
Investment analysis
Loan defaults
Loans
Loss
loss distribution
Loss reserves
Macroeconomic modeling
Macroeconomics
Management science
Modeling
Operational research and scientific management
Operational research. Management science
Portfolio management
Portfolio theory
Probability
Probability distribution
Random variables
recovery rates
Regulation of financial institutions
Regulatory approval
Seniority
Studies
Telecommunications
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Summary: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.
ISSN:0025-1909
1526-5501
DOI:10.1287/mnsc.1110.1345