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Developing an Impairment Loss Given Default Model Using Weighted Logistic Regression Illustrated on a Secured Retail Bank Portfolio

Author

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  • Douw Gerbrand Breed

    (Centre for Business Mathematics and Informatics, North-West University, Potchefstroom 2531, South Africa)

  • Tanja Verster

    (Centre for Business Mathematics and Informatics, North-West University, Potchefstroom 2531, South Africa)

  • Willem D. Schutte

    (Centre for Business Mathematics and Informatics, North-West University, Potchefstroom 2531, South Africa)

  • Naeem Siddiqi

    (SAS Institute Canada, Toronto, ON M5A 1K7, Canada)

Abstract

This paper proposes a new method to model loss given default (LGD) for IFRS 9 purposes. We develop two models for the purposes of this paper—LGD1 and LGD2. The LGD1 model is applied to the non-default (performing) accounts and its empirical value based on a specified reference period using a lookup table. We also segment this across the most important variables to obtain a more granular estimate. The LGD2 model is applied to defaulted accounts and we estimate the model by means of an exposure weighted logistic regression. This newly developed LGD model is tested on a secured retail portfolio from a bank. We compare this weighted logistic regression (WLR) (under the assumption of independence) with generalised estimating equations (GEEs) to test the effects of disregarding the dependence among the repeated observations per account. When disregarding this dependence in the application of WLR, the standard errors of the parameter estimates are underestimated. However, the practical effect of this implementation in terms of model accuracy is found to be negligible. The main advantage of the newly developed methodology is the simplicity of this well-known approach, namely logistic regression of binned variables, resulting in a scorecard format.

Suggested Citation

  • Douw Gerbrand Breed & Tanja Verster & Willem D. Schutte & Naeem Siddiqi, 2019. "Developing an Impairment Loss Given Default Model Using Weighted Logistic Regression Illustrated on a Secured Retail Bank Portfolio," Risks, MDPI, vol. 7(4), pages 1-16, December.
    • Handle: RePEc:gam:jrisks:v:7:y:2019:i:4:p:123-:d:297441
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    References listed on IDEAS

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    1. Thomas, Lyn C., 2009. "Consumer Credit Models: Pricing, Profit and Portfolios," OUP Catalogue, Oxford University Press, number 9780199232130.
    2. Anderson, Raymond, 2007. "The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Management and Decision Automation," OUP Catalogue, Oxford University Press, number 9780199226405.
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    Cited by:

    1. Morne Joubert & Tanja Verster & Helgard Raubenheimer & Willem D. Schutte, 2021. "Adapting the Default Weighted Survival Analysis Modelling Approach to Model IFRS 9 LGD," Risks, MDPI, vol. 9(6), pages 1-17, June.
    2. Haosheng Chen & Daniel Tse & Pengfei Si & Gefei Gao & Chang Yin, 2021. "Strengthen the Security Management of Customer Information in the Virtual Banks of Hong Kong through Business Continuity Management to Maintain Its Business Sustainability," Sustainability, MDPI, vol. 13(19), pages 1-24, September.

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