Modeling Loss Given Default Regressions

32 Pages Posted: 7 Jan 2021

See all articles by Phillip Li

Phillip Li

Federal Deposit Insurance Corporation

Xiaofei Zhang

Government of the United States of America - Office of the Comptroller of the Currency (OCC)

Xinlei Zhao

Government of the United States of America - Office of the Comptroller of the Currency (OCC)

Date Written: December 9, 2020

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.
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Keywords: risk management, loss given default (LGD), bimodal distribution, simulation, predicted distribution, stress testing

JEL Classification: G21, G28

Suggested Citation

Li, Phillip and Zhang, Xiaofei and Zhao, Xinlei, Modeling Loss Given Default Regressions (December 9, 2020). Journal of Risk, Vol. 23, No. 1, October 2020, Pages: 1-32, Available at SSRN: https://ssrn.com/abstract=3761872

Phillip Li (Contact Author)

Federal Deposit Insurance Corporation ( email )

550 17th Street NW
Washington, DC 20429
United States
202-898-3501 (Phone)
202-898-3500 (Fax)

HOME PAGE: http://https://www.fdic.gov/bank/analytical/cfr/bios/li.html

Xiaofei Zhang

Government of the United States of America - Office of the Comptroller of the Currency (OCC) ( email )

400 7th Street SW
Washington, DC 20219
United States

Xinlei Zhao

Government of the United States of America - Office of the Comptroller of the Currency (OCC) ( email )

400 7th Street SW
Washington, DC 20219
United States

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