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Cost-sensitive classification for rare events: an application to the credit rating model validation for SMEs

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  • Raffaella Calabrese

    (Dynamics Lab, Geary Institute, University College Dublin)

Abstract

Receiver Operating Characteristic (ROC) curve is used to assess the discriminatory power of credit rating models. To identify the optimal threshold on the ROC curve, the iso-performance lines are used. The ROC curve and the iso-performance line assume equal classification error costs and that the two classification groups are relatively balanced. These assumptions are unrealistic in the application to credit risk. In order to remove these hypotheses, the curve of Classification Error Costs is proposed. Coherent with this curve, a methodology to identify the optimal threshold is suggested. Monte Carlo simulations that reproduce similar characteristics to the empirical credit scoring models for SMEs show that our proposal performs better that the iso-performance line. Finally, we apply the suggested methodologies to empirical data on Italian Small and Medium Enterprises (SMEs).

Suggested Citation

  • Raffaella Calabrese, 2011. "Cost-sensitive classification for rare events: an application to the credit rating model validation for SMEs," Working Papers 201134, Geary Institute, University College Dublin.
  • Handle: RePEc:ucd:wpaper:201134
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    File URL: http://www.ucd.ie/geary/static/publications/workingpapers/gearywp201134.pdf
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    References listed on IDEAS

    as
    1. Dirk Tasche, 2006. "Validation of internal rating systems and PD estimates," Papers physics/0606071, arXiv.org.
    2. Raffaella Calabrese & Silvia Angela Osmetti, 2011. "Generalized Extreme Value Regression for Binary Rare Events Data: an Application to Credit Defaults," Working Papers 201120, Geary Institute, University College Dublin.
    3. Calabrese, Raffaella & Zenga, Michele, 2010. "Bank loan recovery rates: Measuring and nonparametric density estimation," Journal of Banking & Finance, Elsevier, vol. 34(5), pages 903-911, May.
    4. Fantazzini, Dean, 2008. "Credit Risk Management," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 12(4), pages 84-137.
    5. Altman, Edward I. & Haldeman, Robert G. & Narayanan, P., 1977. "ZETATM analysis A new model to identify bankruptcy risk of corporations," Journal of Banking & Finance, Elsevier, vol. 1(1), pages 29-54, June.
    6. Dirk Tasche, 2002. "Remarks on the monotonicity of default probabilities," Papers cond-mat/0207555, arXiv.org.
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