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A practical approach to validating a PD model

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  • Medema, Lydian
  • Koning, Ruud H.
  • Lensink, Robert

Abstract

The capital adequacy framework Basel II aims to promote the adoption of stronger risk management practices by the banking industry. The implementation makes validation of credit risk models more important. Lenders therefore need a validation methodology to convince their supervisors that their credit scoring models are performing well. In this paper we take up the challenge to propose and implement a simple validation methodology that can be used by banks to validate their credit risk modelling exercise. We will contextualise the proposed methodology by applying it to a default model of mortgage loans of a commercial bank in the Netherlands.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Banking & Finance.

Volume (Year): 33 (2009)
Issue (Month): 4 (April)
Pages: 701-708

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Handle: RePEc:eee:jbfina:v:33:y:2009:i:4:p:701-708

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Web page: http://www.elsevier.com/locate/jbf

Related research

Keywords: Credit risk Probability of default Basel II Statistical validation Logit model;

References

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  1. Jürg M. Blum, 2007. "Why 'Basel II' May Need a Leverage Ratio Restriction," Working Papers 2007-04, Swiss National Bank.
  2. McCullough, B. D. & McGeary, Kerry Anne & Harrison, Teresa D., 2006. "Lessons from the JMCB Archive," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(4), pages 1093-1107, June.
  3. Carling, Kenneth & Jacobson, Tor & Linde, Jesper & Roszbach, Kasper, 2007. "Corporate credit risk modeling and the macroeconomy," Journal of Banking & Finance, Elsevier, vol. 31(3), pages 845-868, March.
  4. Dwyer, Douglas W. & Stein, Roger M., 2006. "Inferring the default rate in a population by comparing two incomplete default databases," Journal of Banking & Finance, Elsevier, vol. 30(3), pages 797-810, March.
  5. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-56, July.
  6. Dewald, William G & Thursby, Jerry G & Anderson, Richard G, 1986. "Replication in Empirical Economics: The Journal of Money, Credit and Banking Project," American Economic Review, American Economic Association, vol. 76(4), pages 587-603, September.
  7. Francis X. Diebold & Celia Chen, 1993. "Testing structural stability with endogenous break point: a size comparison of analytic and bootstrap procedures," Working Papers 93-11, Federal Reserve Bank of Philadelphia.
  8. J. S. Cramer, 2004. "Scoring bank loans that may go wrong: a case study," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 58(3), pages 365-380.
  9. Blochlinger, Andreas & Leippold, Markus, 2006. "Economic benefit of powerful credit scoring," Journal of Banking & Finance, Elsevier, vol. 30(3), pages 851-873, March.
  10. Stein, Roger M., 2005. "The relationship between default prediction and lending profits: Integrating ROC analysis and loan pricing," Journal of Banking & Finance, Elsevier, vol. 29(5), pages 1213-1236, May.
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Citations

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Cited by:
  1. Clara Cardone-Riportella & Antonio Trujillo-Ponce & Anahí Briozzo, 2013. "Analyzing the role of mutual guarantee societies on bank capital requirements for small and medium-sized enterprises," Journal of Economic Policy Reform, Taylor & Francis Journals, vol. 16(2), pages 142-159, June.
  2. Dragoş Bolocan & Cristian Litan, 2011. "Estimating the Probability of Default with Applications in Provisioning the Portfolio of Clients of a Credit Institution," Transition Studies Review, Springer, vol. 18(2), pages 271-285, December.
  3. Rosen, Dan & Saunders, David, 2010. "Risk factor contributions in portfolio credit risk models," Journal of Banking & Finance, Elsevier, vol. 34(2), pages 336-349, February.
  4. Aussenegg, Wolfgang & Resch, Florian & Winkler, Gerhard, 2011. "Pitfalls and remedies in testing the calibration quality of rating systems," Journal of Banking & Finance, Elsevier, vol. 35(3), pages 698-708, March.

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