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. Copyright Infopro Digital Limited. All rights reserved.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.