Performance of credit risk prediction models via proper loss functions

Silvia Figiani () and Mario Maggi ()
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Silvia Figiani: Department of Political and Social Sciences, University of Pavia
Mario Maggi: Department of Economics and Management, University of Pavia

No 64, DEM Working Papers Series from University of Pavia, Department of Economics and Management

Abstract: The performance of predictions models can be assessed using a variety of methods and metrics. Several new measures have recently been proposed that can be seen as refinements of discrimination measures, including variants of the AUC (Area Under the ROC curve), such as the H index. It is widely recognized that AUC suffers from lack of coherency especially when ROC curves cross. On the other hand, the H index requires subjective choices. In our opinion the problem of model comparison should be more adequately handled using a different approach. The main contribution of this paper is to evaluate the performance of prediction models using proper loss function. In order to compare how our approach works with respect to classical measures employed in model comparison, we propose a simulation studies, as well as a real application on credit risk data.

Keywords: Model Comparison; AUC; H index; Loss Function; Proper Scoring Rules; Credit Risk (search for similar items in EconPapers)
Pages: 11 pages
Date: 2014-01
New Economics Papers: this item is included in nep-ban, nep-ecm, nep-for, nep-rmg and nep-upt
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