Bayesian Inference for Predicting the Default Rate Using the Power Prior

Abstract

Commercial banks and other related areas have developed internal models to beter quantify their financial risks. Since an appropriate credit risk model plays a very important role in the risk at financial institutions, it needs more accurate model which forecasts the credit loses, and statistical inference on that model is required. In this paper, we propose a new method for estimating a default rate. It is a Bayesian approach using the power prior which allows for incorporating of historical data to estimate the default rate. Inference on current data could be more reliable if there exist similar data based on previous studies. Ibrahim and Chen (2000) utilize these data to characterize the power prior. It allows for incorporating of historical data to estimate the parameters in the models. We demonstrate our methodologies with a real data set regarding SOHO data and also perform a simulation study.