Further Investigation of Parametric Loss Given Default Modeling

31 Pages Posted: 18 Oct 2016

See all articles by Phillip Li

Phillip Li

Federal Deposit Insurance Corporation

Min Qi

Office of the Comptroller of the Currency - Credit Risk Analysis Division

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: October 17, 2016

Abstract

We conduct a comprehensive study of some parametric models that are designed to fit the unusual bounded and bimodal distribution of loss given default (LGD). We first examine a smearing estimator, a Monte Carlo estimator and a global adjustment approach to refine transformation regression models that address issues with LGD boundary values. Although these refinements only marginally improve model performance, the smearing and Monte Carlo estimators help to reduce the sensitivity of transformation regressions to the adjustment factor. We then conduct a horse race among the refined transformation methods, five parametric models that are specifically suitable for LGD modeling (two-step, inflated beta, Tobit, censored gamma and two-tiered gamma regressions), fractional response regression and standard linear regression. We find that the sophisticated parametric models do not clearly outperform the simpler ones in either predictive accuracy or rank-ordering ability, in-sample, out-of-sample or out of time. Therefore, it is important for modelers and researchers to choose the model that is appropriate for their particular data set, considering differences in model complexity, computational burden, ease of implementation and model performance.

Keywords: bimodal distribution estimation, retransformation methods, gamma regression, inflated beta regression, two-step regression

Suggested Citation

Li, Phillip and Qi, Min and Zhang, Xiaofei and Zhao, Xinlei, Further Investigation of Parametric Loss Given Default Modeling (October 17, 2016). Journal of Credit Risk, Forthcoming. Available at SSRN: https://ssrn.com/abstract=2853325

Phillip Li

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

Min Qi

Office of the Comptroller of the Currency - Credit Risk Analysis Division ( email )

250 E Street, SW
Washington, DC 20219
United States

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 (Contact Author)

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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