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The stability of survival model parameter estimates for predicting the probability of default: Empirical evidence over the credit crisis

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  • Leow, Mindy
  • Crook, Jonathan

Abstract

Using a large portfolio of credit card loans observed between 2002 and 2011 provided by a major UK bank, we investigate the stability of the parameter estimates of discrete survival models, especially since the start of the credit crisis of 2008. Two survival models are developed for accounts that were accepted before and since the crisis. We find that the two sets of parameter estimates are statistically different from each other. By applying the estimated parameters onto a common test set, we also show that they give different predictions of probabilities of default. The changes in the predicted probability distributions are then investigated. We theorise them to be due to the quality of the cohort accepted under different economic conditions, or due to the drastically different economic conditions that was seen in the UK economy, or a combination of both. We test for each effect.

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  • Leow, Mindy & Crook, Jonathan, 2016. "The stability of survival model parameter estimates for predicting the probability of default: Empirical evidence over the credit crisis," European Journal of Operational Research, Elsevier, vol. 249(2), pages 457-464.
    • Handle: RePEc:eee:ejores:v:249:y:2016:i:2:p:457-464
    DOI: 10.1016/j.ejor.2014.09.005
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    Citations

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    Cited by:

    1. Alexandre, Michel & Antônio Silva Brito, Giovani & Cotrim Martins, Theo, 2017. "Default contagion among credit modalities: evidence from Brazilian data," MPRA Paper 76859, University Library of Munich, Germany.
    2. 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.
    3. Medina-Olivares, Victor & Lindgren, Finn & Calabrese, Raffaella & Crook, Jonathan, 2023. "Joint models of multivariate longitudinal outcomes and discrete survival data with INLA: An application to credit repayment behaviour," European Journal of Operational Research, Elsevier, vol. 310(2), pages 860-873.
    4. Luong, Thi Mai & Scheule, Harald, 2022. "Benchmarking forecast approaches for mortgage credit risk for forward periods," European Journal of Operational Research, Elsevier, vol. 299(2), pages 750-767.
    5. Li, Libo, 2018. "Predicting online invitation responses with a competing risk model using privacy-friendly social event data," European Journal of Operational Research, Elsevier, vol. 270(2), pages 698-708.
    6. Bocchio, Cecilia & Crook, Jonathan & Andreeva, Galina, 2023. "The impact of macroeconomic scenarios on recurrent delinquency: A stress testing framework of multi-state models for mortgages," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1655-1677.
    7. Chrysovalantis Gaganis & Panagiota Papadimitri & Fotios Pasiouras & Menelaos Tasiou, 2023. "Social traits and credit card default: a two-stage prediction framework," Annals of Operations Research, Springer, vol. 325(2), pages 1231-1253, June.
    8. Dendramis, Y. & Tzavalis, E. & Varthalitis, P. & Athanasiou, E., 2020. "Predicting default risk under asymmetric binary link functions," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1039-1056.
    9. Lore Dirick & Gerda Claeskens & Bart Baesens, 2017. "Time to default in credit scoring using survival analysis: a benchmark study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(6), pages 652-665, June.
    10. Calabrese, Raffaella & Crook, Jonathan, 2020. "Spatial contagion in mortgage defaults: A spatial dynamic survival model with time and space varying coefficients," European Journal of Operational Research, Elsevier, vol. 287(2), pages 749-761.
    11. 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 Dec 2023.

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