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dc.contributor.authorJoubert, M.
dc.contributor.authorVerster, T.
dc.contributor.authorRaubenheimer, H.
dc.date.accessioned2019-07-12T12:03:50Z
dc.date.available2019-07-12T12:03:50Z
dc.date.issued2018
dc.identifier.citationJoubert, M. et al. 2018. Making use of survival analysis to indirectly model loss given default. Orion, 34(2):107-132. [https:///doi.org/10.5784/34-2-588]en_US
dc.identifier.issn2224-0004
dc.identifier.issn0259-191X (Online)
dc.identifier.urihttp://hdl.handle.net/10394/32881
dc.identifier.urihttp://orion.journals.ac.za/pub/article/view/588
dc.identifier.urihttps://doi.org/10.5784/34-2-588
dc.description.abstractA 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 periodsen_US
dc.language.isoenen_US
dc.publisherStellenbosch Univen_US
dc.subjectLoss given defaulten_US
dc.subjectSurvival analysisen_US
dc.subjectIndirect approachen_US
dc.subjectBaselen_US
dc.titleMaking use of survival analysis to indirectly model loss given defaulten_US
dc.typeArticleen_US
dc.contributor.researchID10943587 - Verster, Tanja
dc.contributor.researchID11937440 - Raubenheimer, Helgard


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