Making use of survival analysis to indirectly model loss given default
Abstract
A direct or indirect modelling methodology can be used to predict Loss Given
Default (LGD). When using the indirect LGD methodology, two components exist,
namely, the loss severity component and the probability component. Commonly used
models to predict the loss severity and the probability component are the haircutand the logistic regression models, respectively. In this article, survival analysis was
proposed as an improvement to the more traditional logistic regression method. The
mean squared error, bias and variance for the two methodologies were compared and
it was shown that the use of survival analysis enhanced the model’s predictive power.
The proposed LGD methodology (using survival analysis) was applied on two simulated datasets and two retail bank datasets, and according to the results obtained it
outperformed the logistic regression LGD methodology. Additional benefits included
that the new methodology could allow for censoring as well as predicting probabilities
over varying outcome periods
URI
http://hdl.handle.net/10394/32881http://orion.journals.ac.za/pub/article/view/588
https://doi.org/10.5784/34-2-588