A Class of Discrete Transformation Survival Models With Application to Default Probability Prediction
A. Adam Ding,
Yan Yu and
Hui Guo ()
Journal of the American Statistical Association, 2012, vol. 107, issue 499, 990-1003
Corporate bankruptcy prediction plays a central role in academic finance research, business practice, and government regulation. Consequently, accurate default probability prediction is extremely important. We propose to apply a discrete transformation family of survival models to corporate default risk predictions. A class of Box-Cox transformations and logarithmic transformations is naturally adopted. The proposed transformation model family is shown to include the popular Shumway model and the grouped relative risk model. We show that a transformation parameter different from those two models is needed for default prediction using a bankruptcy dataset. In addition, we show using out-of-sample validation statistics that our model improves performance. We use the estimated default probability to examine a popular asset pricing question and determine whether default risk has carried a premium. Due to some distinct features of the bankruptcy application, the proposed class of discrete transformation survival models with time-varying covariates is different from the continuous survival models in the survival analysis literature. Their similarities and differences are discussed.
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