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A Class of Discrete Transformation Survival Models With Application to Default Probability Prediction

Listed author(s):
  • A. Adam Ding
  • Shaonan Tian
  • Yan Yu
  • Hui Guo
Registered author(s):

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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Article provided by Taylor & Francis Journals in its journal Journal of the American Statistical Association.

Volume (Year): 107 (2012)
Issue (Month): 499 (September)
Pages: 990-1003

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Handle: RePEc:taf:jnlasa:v:107:y:2012:i:499:p:990-1003
DOI: 10.1080/01621459.2012.682806
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