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Threshold Accepting for Credit Risk Assessment and Validation

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  • Marianna Lyra
  • Akwum Onwunta
  • Peter Winker

Abstract

According to the latest Basel framework of Banking Supervision, financial institutions should internally assign their borrowers into a number of homogeneous groups. Each group is assigned a probability of default which distinguishes it from other groups. This study aims at determining the optimal number and size of groups that allow for statistical ex post validation of the efficiency of the credit risk assignment system. Our credit risk assignment approach is based on Threshold Accepting, a local search optimization technique, which has recently performed reliably in credit risk clustering especially when considering several realistic constraints. Using a relatively large real-world retail credit portfolio, we propose a new technique to validate ex post the precision of the grading system.

Suggested Citation

  • Marianna Lyra & Akwum Onwunta & Peter Winker, 2010. "Threshold Accepting for Credit Risk Assessment and Validation," Working Papers 039, COMISEF.
    • Handle: RePEc:com:wpaper:039
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    References listed on IDEAS

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    1. Michel Dietsch, 2004. "Should SME exposures be treated as retail or corporate exposures: a comparative analysis of probabilities of default and assets correlations in French and German SMEs," ULB Institutional Repository 2013/14164, ULB -- Universite Libre de Bruxelles.
    2. Krink, Thiemo & Paterlini, Sandra & Resti, Andrea, 2008. "The optimal structure of PD buckets," Journal of Banking & Finance, Elsevier, vol. 32(10), pages 2275-2286, October.
    3. Lyra, M. & Paha, J. & Paterlini, S. & Winker, P., 2010. "Optimization heuristics for determining internal rating grading scales," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2693-2706, November.
    4. Winker, Peter & Fang, Kai-Tai, 1995. "Application of threshold accepting to the evaluation of the discrepancy of a set of points," Discussion Papers, Series II 248, University of Konstanz, Collaborative Research Centre (SFB) 178 "Internationalization of the Economy".
    5. Gunter Dueck & Peter Winker, 1992. "New concepts and algorithms for portfolio choice," Applied Stochastic Models and Data Analysis, John Wiley & Sons, vol. 8(3), pages 159-178, September.
    6. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
    7. Dietsch, Michel & Petey, Joel, 2004. "Should SME exposures be treated as retail or corporate exposures? A comparative analysis of default probabilities and asset correlations in French and German SMEs," Journal of Banking & Finance, Elsevier, vol. 28(4), pages 773-788, April.
    8. Krink, Thiemo & Paterlini, Sandra & Resti, Andrea, 2007. "Using differential evolution to improve the accuracy of bank rating systems," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 68-87, September.
    9. Varetto, Franco, 1998. "Genetic algorithms applications in the analysis of insolvency risk," Journal of Banking & Finance, Elsevier, vol. 22(10-11), pages 1421-1439, October.
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    Cited by:

    1. Marianna Lyra, 2010. "Heuristic Strategies in Finance – An Overview," Working Papers 045, COMISEF.

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    Keywords

    credit risk assignment; Threshold Accepting; statistical validation;
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