Comparison of modeling methods for Loss Given Default

Min Qi and Xinlei Zhao

Journal of Banking & Finance, 2011, vol. 35, issue 11, 2842-2855

Abstract: We compare six modeling methods for Loss Given Default (LGD). We find that non-parametric methods (regression tree and neural network) perform better than parametric methods both in and out of sample when over-fitting is properly controlled. Among the parametric methods, fractional response regression has a slight edge over OLS regression. Performance of the transformation methods (inverse Gaussian and beta transformation) is very sensitive to [epsilon], a small adjustment made to LGDs of 0 or 1 prior to transformation. Model fit is poor when [epsilon] is too small or too large, although the fitted LGDs have strong bi-modal distribution with very small [epsilon]. Therefore, models that produce strong bi-model pattern do not necessarily have good model fit and accurate LGD predictions. Even with an optimal [epsilon], the performance of the transformation methods can only match that of the OLS.

Keywords: Loss; Given; Default; (LGD); Regression; tree; Neural; network; Fractional; response; regression; Inverse; Gaussian; regression; Beta; transformation (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (76)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbfina:v:35:y:2011:i:11:p:2842-2855

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