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Performance of credit risk prediction models via proper loss functions

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Author Info

  • Silvia Figini

    ()
    (Department of Political and Social Sciences, University of Pavia)

  • Mario Maggi

    ()
    (Department of Economics and Management, University of Pavia)

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    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.

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    File URL: http://economia.unipv.it/docs/dipeco/quad/ps/RePEc/pav/demwpp/DEMWP0064.pdf
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    Bibliographic Info

    Paper provided by University of Pavia, Department of Economics and Management in its series DEM Working Papers Series with number 064.

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    Length: 11 pages
    Date of creation: Jan 2014
    Date of revision:
    Handle: RePEc:pav:demwpp:demwp0064

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    Related research

    Keywords: Model Comparison; AUC; H index; Loss Function; Proper Scoring Rules; Credit Risk;

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    1. Crook, Jonathan N. & Edelman, David B. & Thomas, Lyn C., 2007. "Recent developments in consumer credit risk assessment," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1447-1465, December.
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