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A traffic lights approach to PD validation

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  • Dirk Tasche

Abstract

As a consequence of the dependence experienced in loan portfolios, the standard binomial test which is based on the assumption of independence does not appear appropriate for validating probabilities of default (PDs). The model underlying the new rules for minimum capital requirements (Basle II) is taken as a point of departure for deriving two parametric test procedures that incorporate dependence effects. The first one makes use of the so-called granularity adjustment approach while the the second one is based on moment matching.

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  • Dirk Tasche, 2003. "A traffic lights approach to PD validation," Papers cond-mat/0305038, arXiv.org.
    • Handle: RePEc:arx:papers:cond-mat/0305038
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    1. Susanne Emmer & Dirk Tasche, 2003. "Calculating credit risk capital charges with the one-factor model," Papers cond-mat/0302402, arXiv.org, revised Jan 2005.
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    Cited by:

    1. 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.
    2. Natalia Nehrebecka, 2017. "Probability-of-default curve calibration and validation of internal rating systems," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Statistical implications of the new financial landscape, volume 43, Bank for International Settlements.
    3. O. Didkovskyi & N. Jean & G. Le Pera & C. Nordio, 2024. "Cross-Domain Behavioral Credit Modeling: transferability from private to central data," Papers 2401.09778, arXiv.org.
    4. Patrick Kurth & Max Nendel & Jan Streicher, 2023. "A hypothesis test for the long-term calibration in rating systems with overlapping time windows," Papers 2312.14765, arXiv.org.
    5. Natalia Nehrebecka, 2021. "COVID-19: stress-testing non-financial companies: a macroprudential perspective. The experience of Poland," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(2), pages 283-319, June.
    6. D Martens & T Van Gestel & M De Backer & R Haesen & J Vanthienen & B Baesens, 2010. "Credit rating prediction using Ant Colony Optimization," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(4), pages 561-573, April.
    7. Stefan Blochwitz & Marcus R. W. Martin & Carsten S. Wehn, 2006. "Statistical Approaches to PD Validation," Springer Books, in: Bernd Engelmann & Robert Rauhmeier (ed.), The Basel II Risk Parameters, chapter 0, pages 289-306, Springer.
    8. Patrick Kurth & Max Nendel & Jan Streicher, 2024. "A Hypothesis Test for the Long-Term Calibration in Rating Systems with Overlapping Time Windows," Risks, MDPI, vol. 12(8), pages 1-28, August.
    9. François Coppens & Fernando Gonzáles & Gerhard Winkler, 2007. "The performance of credit rating systems in the assessment of collateral used in Eurosystem monetary policy operations," Working Paper Research 118, National Bank of Belgium.
    10. Emilia ?I?AN & Adela Ioana TUDOR, 2011. "Conceptual and Statistical Issues Regarding the Probability of Default and Modeling Default Risk," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 2(1), pages 13-22, March.
    11. A. R. Provenzano & D. Trifir`o & A. Datteo & L. Giada & N. Jean & A. Riciputi & G. Le Pera & M. Spadaccino & L. Massaron & C. Nordio, 2020. "Machine Learning approach for Credit Scoring," Papers 2008.01687, arXiv.org.
    12. Casellina, Simone & Pandolfo, Giuseppe & Quagliariello, Mario, 2020. "Applying the Pre-Commitment Approach to bottom-up stress tests: A new old story," Journal of Economics and Business, Elsevier, vol. 112(C).

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