A Survival Model for Wilful Default Prediction – Bayesian Approach

Abstract

This study develops an insolvency model to predict the possible wilful non-payment of debt obligations that turn into bad assets. This paper reveals that financially weak firms have been in deep financial distress somewhere between two to three years prior to their declaration as a wilful defaulter by the initial credit institution and its reporting on the same to the credit information companies. The Cox proportional hazards model (PHM) has been employed, which is a well-known and profusely applied approach not just in medical science but also in forecasting firm bankruptcy. This widely recognized model has been utilized to estimate the effects of different covariates influencing the times-to-event data. The application of Bayesian methods has the benefit in dealing with censored data in small sample over frequentists’ approach. Herein, Bayesian survival framework is applied incorporating normal priors that generally performs better than the traditional likelihood estimation to forecast wilful default. Subsequently, Markov Chain Monte Carlo (MCMC) sampling enables to provide the Bayesian estimator. In the Bayesian structure, the Survival model is used with the help of hazard function. The gamma distribution is selected as the prior for the standard hazard equation in PHM. In order to solve for posterior distribution, the Metropolis Hastings scheme is followed that avoids solving complicated equations with OpenBugs platform.