Comparison of modeling methods for Loss Given Default
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.
Year of publication: |
2011
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Authors: | Qi, Min ; Zhao, Xinlei |
Published in: |
Journal of Banking & Finance. - Elsevier, ISSN 0378-4266. - Vol. 35.2011, 11, p. 2842-2855
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Publisher: |
Elsevier |
Keywords: | Loss Given Default (LGD) Regression tree Neural network Fractional response regression Inverse Gaussian regression Beta transformation |
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