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Estimating Probabilities of Default for Low Default Portfolios

In: The Basel II Risk Parameters

Author

Listed:
  • Katja Pluto

    (Department of Deutsche Bundesbank)

  • Dirk Tasche

    (Department of Deutsche Bundesbank)

Abstract

For credit risk management purposes in general, and for allocation of regulatory capital by banks in particular (Basel II), numerical assessments of the credit-worthiness of borrowers are indispensable. These assessments are expressed in terms of probabilities of default (PD) that should incorporate a certain degree of conservatism in order to reflect the prudential risk management style banks are required to apply. In case of credit portfolios that did not at all suffer defaults, or very few defaults only over years, the resulting naive zero or close to zero estimates would clearly not involve such a sufficient conservatism. As an attempt to overcome this issue, we suggest the "most prudent estimation" principle. This means to estimate the PDs by upper confidence bounds while guaranteeing at the same time a PD ordering that respects the differences in credit quality indicated by the rating grades. The methodology is most easily applied under an assumption of independent default events but can be adapted to the case of correlated defaults.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Katja Pluto & Dirk Tasche, 2006. "Estimating Probabilities of Default for Low Default Portfolios," Springer Books, in: Bernd Engelmann & Robert Rauhmeier (ed.), The Basel II Risk Parameters, chapter 0, pages 79-103, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-33087-5_5
    DOI: 10.1007/3-540-33087-9_5
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    Cited by:

    1. R Florez-Lopez, 2010. "Effects of missing data in credit risk scoring. A comparative analysis of methods to achieve robustness in the absence of sufficient data," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 486-501, March.
    2. Yi-Ping Chang & Chih-Tun Yu, 2014. "Bayesian confidence intervals for probability of default and asset correlation of portfolio credit risk," Computational Statistics, Springer, vol. 29(1), pages 331-361, February.
    3. Jinyu Yang & Weiguo Zhang & Donglai Li, 2015. "Pricing Model for Financial Guaranty Products Using Actuarial Methodology and Most Prudent Principle," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 6(1), pages 101-110, January.
    4. J. Crook & T. Bellotti, 2012. "Asset correlations for credit card defaults," Applied Financial Economics, Taylor & Francis Journals, vol. 22(2), pages 87-95, January.
    5. Rungporn Roengpitya, 2012. "Proposal of New Hybrid PD Estimation Models for the Low Default Portfolios (LDPs), Empirical Comparisons and Policy Implications," Working Papers 2012-03, Monetary Policy Group, Bank of Thailand.
    6. Tasche, Dirk, 2013. "Bayesian estimation of probabilities of default for low default portfolios," Journal of Risk Management in Financial Institutions, Henry Stewart Publications, vol. 6(3), pages 302-326, July.
    7. Steffi Höse & Stefan Huschens, 2011. "Confidence Intervals for Asset Correlations in the Asymptotic Single Risk Factor Model," Operations Research Proceedings, in: Bo Hu & Karl Morasch & Stefan Pickl & Markus Siegle (ed.), Operations Research Proceedings 2010, pages 111-116, Springer.
    8. Rungporn Roengpitya & Pratabjai Nilla-or, 2012. "Challenges on the Validation of PD Models for Low Default Portfolios (LDPs) and Regulatory Policy Implications," Working Papers 2012-02, Monetary Policy Group, Bank of Thailand.
    9. Gourieroux, Christian & Tiomo, Andre, 2019. "The Evaluation of Model Risk for Probability of Default and Expected Loss," MPRA Paper 95795, University Library of Munich, Germany.
    10. Crook, Jonathan N. & Edelman, David B. & Thomas, Lyn C., 2007. "Recent developments in consumer credit risk assessment," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1447-1465, December.
    11. P Beling & G Overstreet & K Rajaratnam, 2010. "Estimation error in regulatory capital requirements: theoretical implications for consumer bank profitability," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 381-392, March.
    12. Andrius Grigutis, 2023. "Probabilistic Overview of Probabilities of Default for Low Default Portfolios by K. Pluto and D. Tasche," Papers 2303.06148, arXiv.org.
    13. Chang Liu & Biqian Zhang & Xuefei Wang & Min Guo, 2022. "Account-level analytic hierarchical mixing modeling for credit risk of Chinese Government financing vehicle portfolios," Empirical Economics, Springer, vol. 62(6), pages 2771-2798, June.
    14. Gustavo F. Serenelli & Emiliano Delfau, 2023. "Estimation of Probabilities for Ordered Sets and Application to Calibration of Rating Models," CEMA Working Papers: Serie Documentos de Trabajo. 849, Universidad del CEMA.

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