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The art of probability-of-default curve calibration

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  • Dirk Tasche

Abstract

ABSTRACT "PD curve calibration" refers to the transformation of a set of rating grade level probabilities of default (PDs) to another average PD level that is determined by a change of the underlying portfolio-wide PD. This paper presents a framework that allows us to explore a variety of calibration approaches and the conditions under which they are fit for purpose. We test the approaches discussed by applying them to publicly available data sets of agency rating and default statistics that can be considered typical for the scope of application of the approaches. We show that the popular "scaled PDs" approach is theoretically questionable and identify an alternative calibration approach ("scaled likelihood ratio") that is both theoretically sound and performs better on the test data sets. ; ;

Suggested Citation

  • Dirk Tasche, . "The art of probability-of-default curve calibration," Journal of Credit Risk, Journal of Credit Risk.
    • Handle: RePEc:rsk:journ1:2310092
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    2. Costola, Michele & Vozian, Katia, 2025. "Pricing climate transition risk: Evidence from European corporate CDS," Energy Economics, Elsevier, vol. 143(C).
    3. Dirk Tasche, 2014. "Exact fit of simple finite mixture models," Papers 1406.6038, arXiv.org, revised Jul 2014.
    4. Dominic Joseph, 2021. "Predicting Credit Default Probabilities Using Bayesian Statistics and Monte Carlo Simulations," Papers 2108.03389, arXiv.org, revised Sep 2021.
    5. Oliver Blümke, 2020. "Estimating the probability of default for no‐default and low‐default portfolios," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(1), pages 89-107, January.
    6. Joël Bessis, 2009. "Risk Management in Banking," Post-Print hal-00494876, HAL.
    7. Douw Gerbrand Breed & Jacques Hurter & Mercy Marimo & Matheba Raletjene & Helgard Raubenheimer & Vibhu Tomar & Tanja Verster, 2023. "A Forward-Looking IFRS 9 Methodology, Focussing on the Incorporation of Macroeconomic and Macroprudential Information into Expected Credit Loss Calculation," Risks, MDPI, vol. 11(3), pages 1-16, March.
    8. Dirk Tasche, 2014. "Exact Fit of Simple Finite Mixture Models," JRFM, MDPI, vol. 7(4), pages 1-15, November.
    9. Brezigar-Masten, Arjana & Masten, Igor & Volk, Matjaž, 2021. "Modelin-g credit risk with a Tobit model of days past due," Journal of Banking & Finance, Elsevier, vol. 122(C).
    10. Baesens, Bart & Smedts, Kristien, 2025. "Boosting credit risk models," The British Accounting Review, Elsevier, vol. 57(4).

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