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Recent Regulation in Credit Risk Management: A Statistical Framework

Author

Listed:
  • Logan Ewanchuk

    (Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2G1, Canada)

  • Christoph Frei

    (Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2G1, Canada)

Abstract

A recently introduced accounting standard, namely the International Financial Reporting Standard 9, requires banks to build provisions based on forward-looking expected loss models. When there is a significant increase in credit risk of a loan, additional provisions must be charged to the income statement. Banks need to set for each loan a threshold defining what such a significant increase in credit risk constitutes. A low threshold allows banks to recognize credit risk early, but leads to income volatility. We introduce a statistical framework to model this trade-off between early recognition of credit risk and avoidance of excessive income volatility. We analyze the resulting optimization problem for different models, relate it to the banking stress test of the European Union, and illustrate it using default data by Standard and Poor’s.

Suggested Citation

  • Logan Ewanchuk & Christoph Frei, 2019. "Recent Regulation in Credit Risk Management: A Statistical Framework," Risks, MDPI, vol. 7(2), pages 1-19, April.
    • Handle: RePEc:gam:jrisks:v:7:y:2019:i:2:p:40-:d:222690
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    References listed on IDEAS

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    1. 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.
    2. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
    3. 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.
    4. Yang, Bill Huajian & Du, Zunwei, 2015. "Stress Testing and Modeling of Rating Migration under the Vasicek Model Framework - Empirical approaches and technical implementation," MPRA Paper 65168, University Library of Munich, Germany.
    5. René M. Stulz, 2015. "Risk-Taking and Risk Management by Banks," Journal of Applied Corporate Finance, Morgan Stanley, vol. 27(1), pages 8-18, March.
    6. Benjamin H Cohen & Gerald A. Edwards, Jr., 2017. "The new era of expected credit loss provisioning," BIS Quarterly Review, Bank for International Settlements, March.
    7. Gordy, Michael B., 2000. "A comparative anatomy of credit risk models," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 119-149, January.
    8. Alexander J. McNeil & Rüdiger Frey & Paul Embrechts, 2015. "Quantitative Risk Management: Concepts, Techniques and Tools Revised edition," Economics Books, Princeton University Press, edition 2, number 10496.
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    Cited by:

    1. Olexandr Yemelyanov & Tetyana Petrushka & Anastasiya Symak & Olena Trevoho & Anatolii Turylo & Oksana Kurylo & Lesia Danchak & Dmytro Symak & Lilia Lesyk, 2020. "Microcredits for Sustainable Development of Small Ukrainian Enterprises: Efficiency, Accessibility, and Government Contribution," Sustainability, MDPI, vol. 12(15), pages 1-32, July.
    2. Emil Ślązak & Magdalena Skwarzec, 2022. "The effects of IFRS 9 valuation model on cost of risk in commercial banks – the impact of COVID-19," Bank i Kredyt, Narodowy Bank Polski, vol. 53(1), pages 47-78.
    3. Paritosh Navinchandra Jha & Marco Cucculelli, 2021. "A New Model Averaging Approach in Predicting Credit Risk Default," Risks, MDPI, vol. 9(6), pages 1-15, June.
    4. Arno Botha & Esmerelda Oberholzer & Janette Larney & Riaan de Jongh, 2023. "Defining and comparing SICR-events for classifying impaired loans under IFRS 9," Papers 2303.03080, arXiv.org, revised Aug 2024.
    5. Ming-Chin Hung & Yung-Kang Ching & Shih-Kuei Lin, 2021. "Impact of COVID-19 on the Robustness of the Probability of Default Estimation Model," Mathematics, MDPI, vol. 9(23), pages 1-13, November.

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