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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.

Suggested Citation

  • Medema, Lydian & Koning, Ruud H. & Lensink, Robert, 2009. "A practical approach to validating a PD model," Journal of Banking & Finance, Elsevier, vol. 33(4), pages 701-708, April.
  • Handle: RePEc:eee:jbfina:v:33:y:2009:i:4:p:701-708
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    Cited by:

    1. 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.
    2. 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.
    3. Douw Gerbrand Breed & Niel van Jaarsveld & Carsten Gerken & Tanja Verster & Helgard Raubenheimer, 2021. "Development of an Impairment Point in Time Probability of Default Model for Revolving Retail Credit Products: South African Case Study," Risks, MDPI, vol. 9(11), pages 1-22, November.
    4. 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.
    5. 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;Central Eastern European University Network (CEEUN), vol. 18(2), pages 271-285, December.
    6. Christopoulos, Andreas D. & Barratt, Joshua G., 2016. "Credit risk findings for commercial real estate loans using the reduced form," Finance Research Letters, Elsevier, vol. 19(C), pages 228-234.
    7. Kritzinger, Nico & van Vuuren, Gary Wayne, 2021. "Non-capital calibration of bureau scorecards," The Quarterly Review of Economics and Finance, Elsevier, vol. 79(C), pages 260-271.
    8. Tsukahara, Fábio Yasuhiro & Kimura, Herbert & Sobreiro, Vinicius Amorim & Zambrano, Juan Carlos Arismendi, 2016. "Validation of default probability models: A stress testing approach," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 70-85.
    9. Christopoulos, Andreas D., 2017. "The composition of CMBS risk," Journal of Banking & Finance, Elsevier, vol. 76(C), pages 215-239.
    10. Kroot, Jan & Giouvris, Evangelos, 2016. "Dutch mortgages: Impact of the crisis on probability of default," Finance Research Letters, Elsevier, vol. 18(C), pages 205-217.
    11. Floros, Ioannis & White, Joshua T., 2016. "Qualified residential mortgages and default risk," Journal of Banking & Finance, Elsevier, vol. 70(C), pages 86-104.
    12. Nehrebecka Natalia, 2018. "An Evaluation of the Discriminatory Power of Selected Polish Bankruptcy Prediction Models As Part of the Validation Process," Financial Sciences. Nauki o Finansach, Sciendo, vol. 23(4), pages 63-88, December.
    13. Kavussanos, Manolis G. & Tsouknidis, Dimitris A., 2016. "Default risk drivers in shipping bank loans," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 94(C), pages 71-94.
    14. Fitzpatrick, Trevor & Mues, Christophe, 2016. "An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market," European Journal of Operational Research, Elsevier, vol. 249(2), pages 427-439.

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