Further Investigation of Parametric Loss Given Default Modeling (WP 2014-2)

Abstract

We conduct a comprehensive study of some new or recently developed parametric methods to estimate loss given default using a common data set. We first propose to use a smearing estimator, a Monte Carlo estimator, and a global adjustment to refine transformation regressions that address loss given default boundary values. Although these refinements only marginally improve model performance, the smearing and Monte Carlo estimators help reduce the sensitivity of transformation regressions to the adjustment factor. We then implement five parametric models (two-step, inflated beta, Tobit, censored gamma, and two-tier gamma regressions) that are not thoroughly studied in the literature but are all designed to fit the unusual bounded bimodal distribution of loss given default. We find that complex parametric models do not necessarily outperform simpler ones, and the non-parametric models may be less computationally burdensome. Our findings suggest that complicated parametric models may not be necessary when estimating loss given default.

Authors

Phillip Li, Min Qi, Xiaofei Zhang, and Xinlei Zhao