IDEAS home Printed from https://ideas.repec.org/p/com/wpaper/039.html
   My bibliography  Save this paper

Threshold Accepting for Credit Risk Assessment and Validation

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://comisef.eu/files/wps039.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

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

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marianna Lyra, 2010. "Heuristic Strategies in Finance – An Overview," Working Papers 045, COMISEF.
    2. Ana Paula Matias Gama & Helena Susana Amaral Geraldes, 2012. "Credit risk assessment and the impact of the New Basel Capital Accord on small and medium-sized enterprises: An empirical analysis," Management Research Review, Emerald Group Publishing, vol. 35(8), pages 727-749, July.
    3. William Gornall & Ilya A. Strebulaev, 2013. "Financing as a Supply Chain: The Capital Structure of Banks and Borrowers," NBER Working Papers 19633, National Bureau of Economic Research, Inc.
    4. Trueck, Stefan & Rachev, Svetlozar T., 2008. "Rating Based Modeling of Credit Risk," Elsevier Monographs, Elsevier, edition 1, number 9780123736833.
    5. Michel Dietsch, 2003. "De Bâle II vers Bâle III : les enjeux et les problèmes du nouvel Accord," Revue d'Économie Financière, Programme National Persée, vol. 73(4), pages 325-342.
    6. Rösch, Daniel & Scheule, Harald, 2009. "The Empirical Relation between Credit Quality, Recovery and Correlation," Hannover Economic Papers (HEP) dp-418, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    7. Filipe, Sara Ferreira & Grammatikos, Theoharry & Michala, Dimitra, 2016. "Forecasting distress in European SME portfolios," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 112-135.
    8. Klaus Duellmann & Jonathan Küll & Michael Kunisch, 2010. "Estimating asset correlations from stock prices or default rates - which method is superior?," Post-Print hal-00736734, HAL.
    9. Magdalena Pisa & Dennis Bams & Christian Wolff, 2012. "Modeling default correlation in a US retail loan portfolio," LSF Research Working Paper Series 12-19, Luxembourg School of Finance, University of Luxembourg.
    10. Düllmann, Klaus & Kunisch, Michael & Küll, Jonathan, 2008. "Estimating asset correlations from stock prices or default rates: which method is superior?," Discussion Paper Series 2: Banking and Financial Studies 2008,04, Deutsche Bundesbank.
    11. Bank for International Settlements, 2011. "Portfolio and risk management for central banks and sovereign wealth funds," BIS Papers, Bank for International Settlements, number 58, June.
    12. Chiara Pederzoli & Costanza Torricelli & Simona Castellani, 2010. "The Interaction of Financial Fragility and the Business Cycle in Determining Banks’ Loan Losses: An Investigation of the Italian Case," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 39(3), pages 129-146, November.
    13. Dietsch, Michel & Düllmann, Klaus & Fraisse, Henri & Koziol, Philipp & Ott, Christine, 2016. "Support for the SME supporting factor: Multi-country empirical evidence on systematic risk factor for SME loans," Discussion Papers 45/2016, Deutsche Bundesbank.
    14. John Nkwoma Inekwe, 2016. "Financial Distress, Employees’ Welfare and Entrepreneurship Among SMEs," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 129(3), pages 1135-1153, December.
    15. 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.
    16. repec:luc:wpaper:13-2 is not listed on IDEAS
    17. Düllmann, Klaus & Koziol, Philipp, 2013. "Evaluation of minimum capital requirements for bank loans to SMEs," Discussion Papers 22/2013, Deutsche Bundesbank.
    18. Lutz Hahnenstein, 2004. "Calibrating the CreditMetrics™ correlation concept — Empirical evidence from Germany," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 18(4), pages 358-381, December.
    19. Daniel Rösch & Harald Scheule, 2014. "Forecasting Mortgage Securitization Risk Under Systematic Risk and Parameter Uncertainty," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 81(3), pages 563-586, September.
    20. Duellmann, Klaus & Küll, Jonathan & Kunisch, Michael, 2010. "Estimating asset correlations from stock prices or default rates--Which method is superior?," Journal of Economic Dynamics and Control, Elsevier, vol. 34(11), pages 2341-2357, November.
    21. 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.

    More about this item

    Keywords

    credit risk assignment; Threshold Accepting; statistical validation;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:com:wpaper:039. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: http://www.comisef.eu .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Anil Khuman (email available below). General contact details of provider: http://www.comisef.eu .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.