The generalized Vasicek credit risk model: A Machine Learning approach

Abstract

This paper explores the ability of the Machine Learning (ML) techniques to calibrate models that replicate the outputs of the Vasicek (1987) credit risk model. In the general case, estimating the loss distribution in this model requires computationally demanding Monte Carlo simulations while the ML approach only requires an initial calibration process. For different granular or concentrated portfolios, our results show that using just two variables (the confidence level and a Gaussian copula-based loss distribution estimate), the tree-based models provide fast and accurate estimates of the real loss distribution.