Assessing Credit Quality from Equity Markets: Is Structural Model a Better Approach?

Abstract

In this paper, we investigate whether the theoretical default probability measures calculated from Merton's (1974) structural credit risk model can provide a better way to explain and predict credit rating than traditional statistical models. The empirical results suggest that Merton's theoretical default measure is not a sufficient statistic of equity market information concerning credit quality. By including the market value of the firm as an independent variable we can improve both in-sample fitting and out-of-sample predictability of credit ratings. Moreover, the empirical performance of this hybrid model is very similar to those simple statistical model. As a result, we conclude that structural models hardly provide any additional capability in capturing credit risk. Our empirical results show that instead of using the firm value only through the debt leverage ratio, as suggested in the structure models, one should include the market value of the firm as a separate factor affecting default probability when building credit risk models.

Keywords: Credit Rating, Default Probability, Distance-to-Default, Structural Credit Risk Model

JEL Classification: G13, G33