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Incorporating heterogeneity and macroeconomic variables into multi-state delinquency models for credit cards

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  • Djeundje, Viani Biatat
  • Crook, Jonathan

Abstract

Multistate delinquency models model the probability that an credit account transits from one state of delinquency to another between any two points in the life of the account. Using a large sample of credit card accounts we parametrise such models with flexible baselines defined in terms of splines, and investigate whether predictive accuracy is enhanced by the incorporation of account specific random effects as well as the incorporation of macroeconomic variables. We conclude that macroeconomic variables are statistically significant in such models, that the inclusion of random effects renders some fixed effects less statistically significant but does not enhance predictive accuracy.

Suggested Citation

  • Djeundje, Viani Biatat & Crook, Jonathan, 2018. "Incorporating heterogeneity and macroeconomic variables into multi-state delinquency models for credit cards," European Journal of Operational Research, Elsevier, vol. 271(2), pages 697-709.
  • Handle: RePEc:eee:ejores:v:271:y:2018:i:2:p:697-709
    DOI: 10.1016/j.ejor.2018.05.040
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    Cited by:

    1. 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.
    2. Yufei Xia & Xinyi Guo & Yinguo Li & Lingyun He & Xueyuan Chen, 2022. "Deep learning meets decision trees: An application of a heterogeneous deep forest approach in credit scoring for online consumer lending," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1669-1690, December.
    3. 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.
    4. Dirick, Lore & Claeskens, Gerda & Vasnev, Andrey & Baesens, Bart, 2022. "A hierarchical mixture cure model with unobserved heterogeneity for credit risk," Econometrics and Statistics, Elsevier, vol. 22(C), pages 39-55.
    5. Jiang, Cuiqing & Wang, Zhao & Zhao, Huimin, 2019. "A prediction-driven mixture cure model and its application in credit scoring," European Journal of Operational Research, Elsevier, vol. 277(1), pages 20-31.
    6. Sigrist, Fabio & Hirnschall, Christoph, 2019. "Grabit: Gradient tree-boosted Tobit models for default prediction," Journal of Banking & Finance, Elsevier, vol. 102(C), pages 177-192.
    7. Victor Medina-Olivares & Finn Lindgren & Raffaella Calabrese & Jonathan Crook, 2023. "Joint model for longitudinal and spatio-temporal survival data," Papers 2311.04008, arXiv.org.
    8. Cedric H. A. Koffi & Viani Biatat Djeundje & Olivier Menoukeu Pamen, 2024. "Impact of social factors on loan delinquency in microfinance," Papers 2410.13100, arXiv.org.

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