IDEAS home Printed from https://ideas.repec.org/a/pal/risman/v23y2021i4d10.1057_s41283-021-00079-2.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41283-021-00079-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1057/s41283-021-00079-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Sirio Aramonte & Fernando Avalos, 2020. "Corporate credit markets after the initial pandemic shock," BIS Bulletins 26, Bank for International Settlements.
    3. Genest, Christian & Nešlehová, Johanna, 2007. "A Primer on Copulas for Count Data," ASTIN Bulletin, Cambridge University Press, vol. 37(2), pages 475-515, November.
    4. 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.
    5. 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.
    6. Bernd Engelmann & Robert Rauhmeier (ed.), 2006. "The Basel II Risk Parameters," Springer Books, Springer, number 978-3-540-33087-5, November.
    7. 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.
    8. 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.
    9. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    10. 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.
    11. Hanson, Samuel & Schuermann, Til, 2006. "Confidence intervals for probabilities of default," Journal of Banking & Finance, Elsevier, vol. 30(8), pages 2281-2301, August.
    12. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
    2. Le, Hong Hanh & Viviani, Jean-Laurent, 2018. "Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios," Research in International Business and Finance, Elsevier, vol. 44(C), pages 16-25.
    3. Serrano-Cinca, Carlos & Gutiérrez-Nieto, Begoña & Bernate-Valbuena, Martha, 2019. "The use of accounting anomalies indicators to predict business failure," European Management Journal, Elsevier, vol. 37(3), pages 353-375.
    4. David Veganzones, 2022. "Corporate failure prediction using threshold‐based models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 956-979, August.
    5. Kumar, Rahul & Deb, Soumya Guha & Mukherjee, Shubhadeep, 2020. "Do words reveal the latent truth? Identifying communication patterns of corporate losers," Journal of Behavioral and Experimental Finance, Elsevier, vol. 26(C).
    6. Rogelio A. Mancisidor & Kjersti Aas, 2022. "Multimodal Generative Models for Bankruptcy Prediction Using Textual Data," Papers 2211.08405, arXiv.org, revised Feb 2024.
    7. Amin Jan & Maran Marimuthu & Muhammad Kashif Shad & Haseeb ur-Rehman & Muhammad Zahid & Ahmad Ali Jan, 2019. "Bankruptcy profile of the Islamic and conventional banks in Malaysia: a post-crisis period analysis," Economic Change and Restructuring, Springer, vol. 52(1), pages 67-87, February.
    8. Mohammad Mahdi Mousavi & Jamal Ouenniche & Kaoru Tone, 2023. "A dynamic performance evaluation of distress prediction models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 756-784, July.
    9. repec:ctc:sdimse:dime19_03 is not listed on IDEAS
    10. Baumöhl, Eduard & Iwasaki, Ichiro & Kočenda, Evžen, 2020. "Firm survival in new EU member states," Economic Systems, Elsevier, vol. 44(1).
    11. repec:ctc:sdimse:dime21_01 is not listed on IDEAS
    12. Tamara Ayœs, Armando Lenin & Villegas, Gladis Cecilia & Leones Castro, María Cristina & Salazar Bocanegra, Juan Antonio, 2018. "Modelaci—n del riesgo de insolvencia en empresas del sector salud empleando modelos logit || Modeling of Insolvency Risk in Health Sector Companies Using Logit Models," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 26(1), pages 128-145, Diciembre.
    13. Soo Young Kim, 2018. "Predicting hospitality financial distress with ensemble models: the case of US hotels, restaurants, and amusement and recreation," Service Business, Springer;Pan-Pacific Business Association, vol. 12(3), pages 483-503, September.
    14. Oliver Lukason & Art Andresson, 2019. "Tax Arrears Versus Financial Ratios in Bankruptcy Prediction," JRFM, MDPI, vol. 12(4), pages 1-13, December.
    15. Tomasz Korol, 2019. "Dynamic Bankruptcy Prediction Models for European Enterprises," JRFM, MDPI, vol. 12(4), pages 1-15, December.
    16. Veganzones, David & Séverin, Eric & Chlibi, Souhir, 2023. "Influence of earnings management on forecasting corporate failure," International Journal of Forecasting, Elsevier, vol. 39(1), pages 123-143.
    17. Fedorova, Elena & Ledyaeva, Svetlana & Drogovoz, Pavel & Nevredinov, Alexandr, 2022. "Economic policy uncertainty and bankruptcy filings," International Review of Financial Analysis, Elsevier, vol. 82(C).
    18. Ligang Zhou & Kin Keung Lai, 2017. "AdaBoost Models for Corporate Bankruptcy Prediction with Missing Data," Computational Economics, Springer;Society for Computational Economics, vol. 50(1), pages 69-94, June.
    19. Jaizah Othman & Mehmet Asutay, 2018. "Integrated early warning prediction model for Islamic banks: the Malaysian case," Journal of Banking Regulation, Palgrave Macmillan, vol. 19(2), pages 118-130, April.
    20. Huang, Chao & Dai, Chong & Guo, Miao, 2015. "A hybrid approach using two-level DEA for financial failure prediction and integrated SE-DEA and GCA for indicators selection," Applied Mathematics and Computation, Elsevier, vol. 251(C), pages 431-441.
    21. Koen W. de Bock, 2017. "The best of two worlds: Balancing model strength and comprehensibility in business failure prediction using spline-rule ensembles," Post-Print hal-01588059, HAL.
    22. Khoja, Layla & Chipulu, Maxwell & Jayasekera, Ranadeva, 2019. "Analysis of financial distress cross countries: Using macroeconomic, industrial indicators and accounting data," International Review of Financial Analysis, Elsevier, vol. 66(C).

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:risman:v:23:y:2021:i:4:d:10.1057_s41283-021-00079-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.