IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v7y2019i4p123-d297441.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/7/4/123/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-9091/7/4/123/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Thomas, Lyn C., 2009. "Consumer Credit Models: Pricing, Profit and Portfolios," OUP Catalogue, Oxford University Press, number 9780199232130.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    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. Douw Gerbrand Breed & Niel van Jaarsveld & Carsten Gerken & Tanja Verster & Helgard Raubenheimer, 2021. "Development of an Impairment Point in Time Probability of Default Model for Revolving Retail Credit Products: South African Case Study," Risks, MDPI, vol. 9(11), pages 1-22, November.
    2. Matuszyk, Anna & So, Mee Chi & Mues, Christophe & Moore, Angela, 2016. "Modelling repayment patterns in the collections process for unsecured consumer debt: A case studyAuthor-Name: Thomas, Lyn C," European Journal of Operational Research, Elsevier, vol. 249(2), pages 476-486.
    3. Martin Řezáč, 2015. "ESIS2: Information Value Estimator for Credit Scoring Models," Computational Economics, Springer;Society for Computational Economics, vol. 45(2), pages 303-322, February.
    4. Li, Aimin & Li, Zhiyong & Bellotti, Anthony, 2023. "Predicting loss given default of unsecured consumer loans with time-varying survival scores," Pacific-Basin Finance Journal, Elsevier, vol. 78(C).
    5. Martin Řezáč, 2011. "Advanced empirical estimate of information value for credit scoring models," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 59(2), pages 267-274.
    6. Martin Rezac & Frantisek Rezac, 2011. "How to Measure the Quality of Credit Scoring Models," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 61(5), pages 486-507, November.
    7. Gustavo Henrique Araujo Pereira & Rinaldo Artes, 2016. "A comparison of strategies to develop a customer default scoring model," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(11), pages 1341-1352, November.
    8. Marcin Chlebus, 2014. "One-day prediction of state of turbulence for financial instrument based on models for binary dependent variable," Ekonomia journal, Faculty of Economic Sciences, University of Warsaw, vol. 37.
    9. Christa N. Gibbs & Benedict Guttman-Kenney & Donghoon Lee & Scott Nelson & Wilbert Van der Klaauw & Jialan Wang, 2024. "Consumer Credit Reporting Data," Staff Reports 1114, Federal Reserve Bank of New York.
    10. R T Stewart, 2011. "A profit-based scoring system in consumer credit: making acquisition decisions for credit cards," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(9), pages 1719-1725, September.
    11. Raffaele Manini & Oriol Amat, 2018. "Credit scoring for the supermarket and retailing industry: analysis and application proposal," Economics Working Papers 1614, Department of Economics and Business, Universitat Pompeu Fabra.
    12. Enrique Batiz‐Zuk & Fabrizio López‐Gallo & Abdulkadir Mohamed & Fátima Sánchez‐Cajal, 2022. "Determinants of loan survival rates for small and medium‐sized enterprises: Evidence from an emerging economy," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4741-4755, October.
    13. A?da Kammoun & Imen Triki, 2016. "Credit Scoring Models for a Tunisian Microfinance Institution: Comparison between Artificial Neural Network and Logistic Regression," Review of Economics & Finance, Better Advances Press, Canada, vol. 6, pages 61-78, February.
    14. Kritzinger, Nico & van Vuuren, Gary Wayne, 2021. "Non-capital calibration of bureau scorecards," The Quarterly Review of Economics and Finance, Elsevier, vol. 79(C), pages 260-271.
    15. Zhiyong Li & Xinyi Hu & Ke Li & Fanyin Zhou & Feng Shen, 2020. "Inferring the outcomes of rejected loans: an application of semisupervised clustering," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 631-654, February.
    16. George Xianzhi Yuan & Huiqi Wang, 2019. "The general dynamic risk assessment for the enterprise by the hologram approach in financial technology," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 6(01), pages 1-48, March.
    17. Crone, Sven F. & Finlay, Steven, 2012. "Instance sampling in credit scoring: An empirical study of sample size and balancing," International Journal of Forecasting, Elsevier, vol. 28(1), pages 224-238.
    18. Ka-Kit Leung & Horas T H Wong & Claire M Naftalin & Shui Shan Lee, 2014. "A New Perspective on Sexual Mixing among Men Who Have Sex with Men by Body Image," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-5, November.
    19. Kiviat, Barbara, 2019. "Credit Scoring in the United States," economic sociology. perspectives and conversations, Max Planck Institute for the Study of Societies, vol. 21(1), pages 33-42.
    20. Singh, Ramendra Pratap & Singh, Ramendra & Mishra, Prashant, 2021. "Does managing customer accounts receivable impact customer relationships, and sales performance? An empirical investigation," Journal of Retailing and Consumer Services, Elsevier, vol. 60(C).

    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:gam:jrisks:v:7:y:2019:i:4:p:123-:d:297441. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.