Comparison of modeling methods for Loss Given Default
AbstractWe 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.
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Banking & Finance.
Volume (Year): 35 (2011)
Issue (Month): 11 (November)
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Web page: http://www.elsevier.com/locate/jbf
Loss Given Default (LGD) Regression tree Neural network Fractional response regression Inverse Gaussian regression Beta transformation;
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