A Credit Risk Model with Dynamic Frailties for Default Intensity Estimation

Abstract

In the spirit of reduced-form models, default is treated as an unpredictable jump with an exogenously specific hazard rate process. Most researchers using reduced-form models assume doubly stochastic properties. In practice, this assumption is annulled in the presence of frailty factors, which are unobservable explanatory variables correlated across firms. However, few if any methods are used to estimate a credit risk model with frailty factors. This paper proposes a maximization likelihood estimation method using a proportional hazard model and an algorithm for implementation. The proportional hazard model is a popular method for analyzing the effect of covariates on the hazard rate. An application to real data is then presented, with the results showing that the model with frailty is more accurate than the model without it.