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IRB PD model accuracy validation in the presence of default correlation: a twin confidence interval approach

Author

Listed:
  • Dmitriy Borzykh

    (HSE University
    National Research University Higher School of Economics)

  • Henry Penikas

    (Bank of Russia
    National Research University Higher School of Economics
    P.N. Lebedev Physics Institute of the Russian Academy of Sciences)

Abstract

The BIS indicated in July 2020 an unprecedented rise in default risk correlation as a result of pandemics-induced credit risks’ accumulation. A third of the world banking assets credit risk measurement depends on the Basel internal-ratings-based (IRB) models. To ensure financial stability, we wish IRB models to be accurate in default probability (PD) forecasting. There naturally arises a question of which model may be deemed accurate if the data demonstrates the presence of the default correlation. The existing prudential IRB validation guidelines suggest a confidence interval of up to 100 percentage points’ length for such a case. Such an interval is useless as any model and any PD forecast seem accurate. The novelty of this paper is the justification for the use of twin confidence intervals to validate PD model accuracy. Those intervals more concentrate around the two extremes (default and its absence), the higher the default correlation is.

Suggested Citation

  • Dmitriy Borzykh & Henry Penikas, 2021. "IRB PD model accuracy validation in the presence of default correlation: a twin confidence interval approach," Risk Management, Palgrave Macmillan, vol. 23(4), pages 282-300, December.
    • Handle: RePEc:pal:risman:v:23:y:2021:i:4:d:10.1057_s41283-021-00079-2
    DOI: 10.1057/s41283-021-00079-2
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    References listed on IDEAS

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    1. Sirio Aramonte & Fernando Avalos, 2020. "Corporate credit markets after the initial pandemic shock," BIS Bulletins 26, Bank for International Settlements.
    2. Genest, Christian & Nešlehová, Johanna, 2007. "A Primer on Copulas for Count Data," ASTIN Bulletin, Cambridge University Press, vol. 37(2), pages 475-515, November.
    3. Gary Witt, 2014. "A Simple Distribution for the Sum of Correlated, Exchangeable Binary Data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 43(20), pages 4265-4280, October.
    4. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    5. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    6. Ravi Kumar, P. & Ravi, V., 2007. "Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review," European Journal of Operational Research, Elsevier, vol. 180(1), pages 1-28, July.
    7. Hanson, Samuel & Schuermann, Til, 2006. "Confidence intervals for probabilities of default," Journal of Banking & Finance, Elsevier, vol. 30(8), pages 2281-2301, August.
    8. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    9. 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.
    10. Li, Xiaofeng & Shang, Ying & Su, Zhi, 2015. "Semiparametric estimation of default probability: Evidence from the Prosper online credit market," Economics Letters, Elsevier, vol. 127(C), pages 54-57.
    11. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    12. Bernd Engelmann & Robert Rauhmeier (ed.), 2006. "The Basel II Risk Parameters," Springer Books, Springer, number 978-3-540-33087-5, June.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Binary choice models; Bernoulli random variables; Default correlation; Confidence interval; Basel III; IRB;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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