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A Forward-Looking IFRS 9 Methodology, Focussing on the Incorporation of Macroeconomic and Macroprudential Information into Expected Credit Loss Calculation

Author

Listed:
  • Douw Gerbrand Breed

    (Centre for Business Mathematics and Informatics, North-West University, Private Bag X6001, Potchefstroom 2520, South Africa)

  • Jacques Hurter

    (Independent Researcher, 116 Cockspur Road, Roodepoort 1709, South Africa)

  • Mercy Marimo

    (Independent Researcher, P.O. Box 613 Heliopolis, Cairo 11757, Egypt)

  • Matheba Raletjene

    (Independent Researcher, 108 Bateleur Str., Midrand 1628, South Africa)

  • Helgard Raubenheimer

    (Centre for Business Mathematics and Informatics, North-West University, Private Bag X6001, Potchefstroom 2520, South Africa
    National Institute for Theoretical and Computational Sciences (NITheCS), Pretoria 0001, South Africa)

  • Vibhu Tomar

    (Independent Researcher, DLF Cyber City, Gurgaon 122002, India)

  • Tanja Verster

    (Centre for Business Mathematics and Informatics, North-West University, Private Bag X6001, Potchefstroom 2520, South Africa
    National Institute for Theoretical and Computational Sciences (NITheCS), Pretoria 0001, South Africa)

Abstract

The International Financial Reporting Standard (IFRS) 9 relates to the recognition of an entity’s financial asset/liability in its financial statement, and includes an expected credit loss (ECL) framework for recognising impairment. The quantification of ECL is often broken down into its three components, namely, the probability of default (PD), loss given default (LGD), and exposure at default (EAD). The IFRS 9 standard requires that the ECL model accommodates the influence of the current and the forecasted macroeconomic conditions on credit loss. This enables a determination of forward-looking estimates on impairments. This paper proposes a methodology based on principal component regression (PCR) to adjust IFRS 9 PD term structures for macroeconomic forecasts. We propose that a credit risk index (CRI) is derived from historic defaults to approximate the default behaviour of the portfolio. PCR is used to model the CRI with the macroeconomic variables as the set of explanatory variables. A novice all-subset variable selection is proposed, incorporating business decisions. We demonstrate the method’s advantages on a real-world banking data set, and compare it to several other techniques. The proposed methodology is on portfolio-level with the recommendation to derive a macroeconomic scalar for each different risk segment of the portfolio. The proposed scalar is intended to adjust loan-level PDs for forward-looking information.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jrisks:v:11:y:2023:i:3:p:59-:d:1096881
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    References listed on IDEAS

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    1. Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
    2. Willem Daniel Schutte & Tanja Verster & Derek Doody & Helgard Raubenheimer & Peet Jacobus Coetzee & David McMillan, 2020. "A proposed benchmark model using a modularised approach to calculate IFRS 9 expected credit loss," Cogent Economics & Finance, Taylor & Francis Journals, vol. 8(1), pages 1735681-173, January.
    3. Michael Jacobs, 2019. "An Analysis of the Impact of Modeling Assumptions in the Current Expected Credit Loss (CECL) Framework on the Provisioning for Credit Loss," Journal of Risk & Control, Risk Market Journals, vol. 6(1), pages 65-112.
    4. Dirk Tasche, 2012. "The art of probability-of-default curve calibration," Papers 1212.3716, arXiv.org, revised Nov 2013.
    5. Bellotti, Tony & Crook, Jonathan, 2013. "Forecasting and stress testing credit card default using dynamic models," International Journal of Forecasting, Elsevier, vol. 29(4), pages 563-574.
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