Predicting Credit Default Probabilities Using Bayesian Statistics and Monte Carlo Simulations

This paper discusses the use of Bayesian principles and simulation-techniques to estimate and calibrate the default probability of credit ratings. The methodology is a two-phase approach where, in the first phase, a posterior density of default rate parameter is estimated based the default history data. In the second phase of the approach, an estimate of true default rate parameter is obtained through simulations. 2021: Dominic Joseph //arxiv.org/pdf/2108.03389.pdf