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Optimization heuristics for determining internal rating grading scales

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  • Lyra, M.
  • Paha, J.
  • Paterlini, S.
  • Winker, P.

Abstract

Basel II imposes regulatory capital on banks related to the default risk of their credit portfolio. Banks using an internal rating approach compute the regulatory capital from pooled probabilities of default. These pooled probabilities can be calculated by clustering credit borrowers into different buckets and computing the mean PD for each bucket. The clustering problem can become very complex when Basel II regulations and real-world constraints are taken into account. Search heuristics have already proven remarkable performance in tackling this problem. A Threshold Accepting algorithm is proposed, which exploits the inherent discrete nature of the clustering problem. This algorithm is found to outperform alternative methodologies already proposed in the literature, such as standard k-means and Differential Evolution. Besides considering several clustering objectives for a given number of buckets, we extend the analysis further by introducing new methods to determine the optimal number of buckets in which to cluster banks' clients.

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Bibliographic Info

Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

Volume (Year): 54 (2010)
Issue (Month): 11 (November)
Pages: 2693-2706

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Handle: RePEc:eee:csdana:v:54:y:2010:i:11:p:2693-2706

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References

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  1. Thiemo Krink & Sandra Paterlini, 2008. "Differential Evolution for Multiobjective Portfolio Optimization," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 08012, Universita di Modena e Reggio Emilia, Facoltà di Economia "Marco Biagi".
  2. 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.
  3. Dietmar Maringer & Peter Winker, 2004. "Optimal Lag Structure Selection in VEC-Models," Computing in Economics and Finance 2004 155, Society for Computational Economics.
  4. 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.
  5. Winker, Peter & Gilli, Manfred, 2004. "Applications of optimization heuristics to estimation and modelling problems," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 211-223, September.
  6. Hunt, Daniel L. & Cheng, Cheng & Pounds, Stanley, 2009. "The beta-binomial distribution for estimating the number of false rejections in microarray gene expression studies," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1688-1700, March.
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Cited by:
  1. Viktoria Blüschke-Nikolaeva & Dmitri Blüschke & Reinhard Neck, 2010. "Optimal Control of Nonlinear Dynamic Econometric Models: An Algorithm and an Application," Working Papers 032, COMISEF.
  2. Marianna Lyra, 2010. "Heuristic Strategies in Finance – An Overview," Working Papers 045, COMISEF.
  3. Stefano Cosma & Elisabetta Gualandri, 2013. "The sovereign debt crisis: the impact on the intermediation model of Italian banks," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 13102, Universita di Modena e Reggio Emilia, Facoltà di Economia "Marco Biagi".
  4. Marianna Lyra & Akwum Onwunta & Peter Winker, 2010. "Threshold Accepting for Credit Risk Assessment and Validation," Working Papers 039, COMISEF.
  5. Schleer, Frauke, 2013. "Finding starting-values for maximum likelihood estimation of vector STAR models," ZEW Discussion Papers 13-076, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  6. Chiara Pederzoli & Costanza Torricelli, 2013. "Efficiency and unbiasedness of corn futures markets: New evidence across the financial crisis," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 13091, Universita di Modena e Reggio Emilia, Facoltà di Economia "Marco Biagi".
  7. Ivan Savin & Dmitri Blueschke, 2013. "Solving nonlinear stochastic optimal control problems using evolutionary heuristic optimization," Jena Economic Research Papers 2013-051, Friedrich-Schiller-University Jena, Max-Planck-Institute of Economics.
  8. Capotorti, Andrea & Barbanera, Eva, 2012. "Credit scoring analysis using a fuzzy probabilistic rough set model," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 981-994.
  9. D. Blueschke & V. Blueschke-Nikolaeva & Ivan Savin, 2012. "New Insights Into Optimal Control of Nonlinear Dynamic Econometric Models: Application of a Heuristic Approach," Jena Economic Research Papers 2012-008, Friedrich-Schiller-University Jena, Max-Planck-Institute of Economics.
  10. Chiara Pederzoli & Costanza Torricelli, 2010. "A parsimonious default prediction model for Italian SMEs," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 10061, Universita di Modena e Reggio Emilia, Facoltà di Economia "Marco Biagi".
  11. Elisabetta Gualandri & Valeria Venturelli, 2013. "The financing of Italian firms and the credit crunch: findings and exit strategies," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 13101, Universita di Modena e Reggio Emilia, Facoltà di Economia "Marco Biagi".

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