Modeling Loss Given Default

51 Pages Posted: 9 Aug 2018 Last revised: 22 Aug 2018

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 Shelly Zhao

Government of the United States of America - Office of the Currency Comptroller - Risk Analysis Division

Date Written: July 1, 2018

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, these models still show similar poor performance in terms of predictive accuracy and rank ordering when mean predictions and squared error loss functions are used. Therefore, the findings in the literature that predictive accuracy and rank ordering cluster in a very narrow range across different parametric models are robust. We argue, however, that predicted distributions as well as the models’ ability to accurately capture marginal effects are also important performance metrics for capital models and stress testing. We find that the sophisticated parametric models that are specifically designed to address the bi-modal distributions of LGD outperform the less sophisticated models by a large margin in terms of predicted distributions. Also, we find that stress testing poses a challenge to all LGD models because of limited data and relevant explanatory variable availability, and that model selection criteria based on goodness of fit may not serve the stress testing purpose well. Finally, the evidence here suggests that we do not need to use the most sophisticated parametric methods to model LGD.

Keywords: loss given default, bi-modal distribution, simulation, predicted distribution, stress testing

JEL Classification: G21, G28

Suggested Citation

Li, Phillip and Zhang, Xiaofei and Zhao, Xinlei, Modeling Loss Given Default (July 1, 2018). FDIC Center for Financial Research Paper No. 2018-03. Available at SSRN: https://ssrn.com/abstract=3229170 or http://dx.doi.org/10.2139/ssrn.3229170

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 Currency Comptroller - Risk Analysis Division ( email )

250 E Street, SW
Washington, DC 20219
United States

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