Generalized Beta Regression Models for Random Loss-Given-Default

by Xinzheng Huang of Delft University of Technology & Rabobank, and
Cornelis W. Oosterlee of Delft University of Technology & CWI

September 9, 2008

Abstract: We propose a new framework for modeling systematic risk in Loss-Given-Default (LGD) in the context of credit portfolio losses. The class of models is very flexible and accommodates well skewness and heteroscedastic errors. The quantities in the models have simple economic interpretation. Inference of models in this framework can be unified. Moreover, it allows efficient numerical procedures, such as the normal approximation and the saddlepoint approximation, to calculate the portfolio loss distribution, Value at Risk (VaR) and Expected Shortfall (ES).

Published in: Journal of Credit Risk, Vol. 7, No. 4, (Winter 2011/2012), pp. 45-70.

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Related reading: LossCalc v2: Dynamic Prediction of LGD