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Modelling credit risk of portfolio of consumer loans

Author

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  • M Malik

    (University of Southampton)

  • L C Thomas

    (University of Southampton)

Abstract

One of the issues that the Basel Accord highlighted was that, though techniques for estimating the probability of default and hence the credit risk of loans to individual consumers are well established, there were no models for the credit risk of portfolios of such loans. Motivated by the reduced form models for credit risk in corporate lending, we seek to exploit the obvious parallels between behavioural scores and the ratings ascribed to corporate bonds to build consumer-lending equivalents. We incorporate both consumer-specific ratings and macroeconomic factors in the framework of Cox Proportional Hazard models. Our results show that default intensities of consumers are significantly influenced by macro factors. Such models then can be used as the basis for simulation approaches to estimate the credit risk of portfolios of consumer loans.

Suggested Citation

  • M Malik & L C Thomas, 2010. "Modelling credit risk of portfolio of consumer loans," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 411-420, March.
    • Handle: RePEc:pal:jorsoc:v:61:y:2010:i:3:d:10.1057_jors.2009.123
    DOI: 10.1057/jors.2009.123
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    References listed on IDEAS

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    1. Duffie, Darrell & Saita, Leandro & Wang, Ke, 2007. "Multi-period corporate default prediction with stochastic covariates," Journal of Financial Economics, Elsevier, vol. 83(3), pages 635-665, March.
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    5. Thomas, Lyn C., 2009. "Consumer Credit Models: Pricing, Profit and Portfolios," OUP Catalogue, Oxford University Press, number 9780199232130.
    6. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
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    3. Dimitris Andriosopoulos & Michalis Doumpos & Panos M. Pardalos & Constantin Zopounidis, 2019. "Computational approaches and data analytics in financial services: A literature review," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1581-1599, October.
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    6. Bernd Engelmann & Ha Pham, 2020. "A Raroc Valuation Scheme for Loans and Its Application in Loan Origination," Risks, MDPI, vol. 8(2), pages 1-20, June.
    7. Medina-Olivares, Victor & Calabrese, Raffaella & Crook, Jonathan & Lindgren, Finn, 2023. "Joint models for longitudinal and discrete survival data in credit scoring," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1457-1473.
    8. Bernd Engelmann & Ha Pham, 2020. "Measuring the Performance of Bank Loans under Basel II/III and IFRS 9/CECL," Risks, MDPI, vol. 8(3), pages 1-21, September.
    9. Li, Zhiyong & Li, Aimin & Bellotti, Anthony & Yao, Xiao, 2023. "The profitability of online loans: A competing risks analysis on default and prepayment," European Journal of Operational Research, Elsevier, vol. 306(2), pages 968-985.
    10. Tong, Edward N.C. & Mues, Christophe & Brown, Iain & Thomas, Lyn C., 2016. "Exposure at default models with and without the credit conversion factor," European Journal of Operational Research, Elsevier, vol. 252(3), pages 910-920.
    11. Adriana Uquillas, 2017. "Determinantes del riesgo comportamental en préstamos de consumo y microcrédito: Un estudio de caso en Centro América," Revista de Investigación en Ciencias Contables y Administrativas, Universidad Michoacana de San Nicolás de Hidalgo, Facultad de Contaduría y Ciencias Administrativas, vol. 3(1), pages 35-66, July.
    12. Joseph L Breeden & Lyn Thomas, 2016. "Solutions to specification errors in stress testing models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(6), pages 830-840, June.
    13. Ju, Yonghan & Jeon, Song Yi & Sohn, So Young, 2015. "Behavioral technology credit scoring model with time-dependent covariates for stress test," European Journal of Operational Research, Elsevier, vol. 242(3), pages 910-919.

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