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Optimization heuristics for determining internal rating grading scales

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Listed:
  • 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.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:csdana:v:54:y:2010:i:11:p:2693-2706
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    References listed on IDEAS

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    1. 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.
    2. 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.
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    Cited by:

    1. Blueschke-Nikolaeva, V. & Blueschke, D. & Neck, R., 2012. "Optimal control of nonlinear dynamic econometric models: An algorithm and an application," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3230-3240.
    2. Blueschke, D. & Blueschke-Nikolaeva, V. & Savin, I., 2013. "New insights into optimal control of nonlinear dynamic econometric models: Application of a heuristic approach," Journal of Economic Dynamics and Control, Elsevier, vol. 37(4), pages 821-837.
    3. Marianna Lyra & Akwum Onwunta & Peter Winker, 2015. "Threshold accepting for credit risk assessment and validation," Journal of Banking Regulation, Palgrave Macmillan, vol. 16(2), pages 130-145, April.
    4. Stefano Cosma & Elisabetta Gualandri, 2014. "The sovereign debt crisis: the impact on the intermediation model of Italian banks," BANCARIA, Bancaria Editrice, vol. 2, pages 48-60, February.
    5. 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, Dipartimento di Economia "Marco Biagi".
    6. Frauke Schleer, 2015. "Finding Starting-Values for the Estimation of Vector STAR Models," Econometrics, MDPI, Open Access Journal, vol. 3(1), pages 1-26, January.
    7. Elisabetta Gualandri & Mario Noera, 2014. "Towards A Macroprudential Policy In The Eu: Main Issues," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 14110, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    8. Carlo Alberto Magni, 2015. "Pseudo-naïve approaches to investment performance measurement," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 15021, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    9. Massimo Baldini & Giovanni Gallo & Costanza Torricelli, 2017. "Past Income Scarcity and Current Perception of Financial Fragility," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 17121, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    10. D. Blueschke & I. Savin, 2017. "No such thing as a perfect hammer: comparing different objective function specifications for optimal control," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 25(2), pages 377-392, June.
    11. Elena Giarda & Gloria Moroni, 2018. "The Degree of Poverty Persistence and the Role of Regional Disparities in Italy in Comparison with France, Spain and the UK," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 136(1), pages 163-202, February.
    12. 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.
    13. Stefano Cosma & Francesca Pancotto & Paola Vezzani, 2018. "Customer Complaining and Probability of Default in Consumer Credit," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 18031, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    14. Ivan Savin & Dmitri Blueschke, 2016. "Lost in Translation: Explicitly Solving Nonlinear Stochastic Optimal Control Problems Using the Median Objective Value," Computational Economics, Springer;Society for Computational Economics, vol. 48(2), pages 317-338, August.
    15. C. Pederzoli & C. Torricelli, 2013. "Efficiency and unbiasedness of corn futures markets: new evidence across the financial crisis," Applied Financial Economics, Taylor & Francis Journals, vol. 23(24), pages 1853-1863, December.
    16. Marianna Lyra, 2010. "Heuristic Strategies in Finance – An Overview," Working Papers 045, COMISEF.
    17. 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.
    18. Elisabetta Gualandri & Mario Noera, 2014. "Monitoring Systemic Risk: A Survey Of The Available Macroprudential Toolkit," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 14111, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    19. Enrico Rubaltelli & Sergio Agnoli & Michela Rancan & Tiziana Pozzoli, 2015. "Emotional Intelligence and risk taking in investment decision-making," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 15107, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    20. 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, Dipartimento di Economia "Marco Biagi".
    21. 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.

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    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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