Modeling loss given default regressions

Phillip Li, Xiaofei Zhang and Xinlei Zhao

Abstract

We investigate the puzzle in the literature that various parametric loss given default (LGD) statistical models perform similarly, by comparing their performance in a simulation framework. We find that, even using the full set of explanatory variables from the assumed data-generating process where noise is minimized, these models still show a similarly poor performance in terms of predictive accuracy and rank-ordering when mean predictions and squared error loss functions are used. However, the sophisticated parametric modes that are specifically designed to address the bimodal distributions of LGD outperform the less sophisticated models by a large margin in terms of predicted distributions. Our results also suggest that stress testing may pose a challenge to all LGD models due to a lack of loss data and the limited availability of relevant explanatory variables, and that model selection criteria based on goodness-of-fit may not serve the stress testing purpose well.