Default Prediction of Various Structural Models

by Ren-Raw Chen of Rutgers University,
Shing-yang Hu of National Taiwan University, and
Ging-Ging Pan of National Ping-Tung University of Sciences and Technologies

July 21, 2006

Abstract: The structural approach of credit risk modeling has gained growing attention in both academics and industry. While there has been increasing effort in testing the structural models for credit derivatives pricing, little result has been shown on the default prediction of the structural models. In this paper, we compare comprehensively various credit structural models for their default prediction capability. We select models that cover distinctly different assumptions such that we can study how and why certain models can predict default better than others. In addition to the well known existing structural models, we also introduce a non-parametric model to study the distributional characteristics underlying the structural models.

Our results indicate that the distribution characteristics of the equity returns and endogenous recovery are two important assumptions. On the other hand, random interest rates that play an important role in pricing credit derivatives are not an important assumption in predicting default.

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