IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v271y2018i2p697-709.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037722171830448X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2018.05.040?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Drew Creal & Bernd Schwaab & Siem Jan Koopman & Andr� Lucas, 2014. "Observation-Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk," The Review of Economics and Statistics, MIT Press, vol. 96(5), pages 898-915, December.
    2. Koopman, Siem Jan & Kräussl, Roman & Lucas, André & Monteiro, André B., 2009. "Credit cycles and macro fundamentals," Journal of Empirical Finance, Elsevier, vol. 16(1), pages 42-54, January.
    3. Koopman, Siem Jan & Lucas, Andre & Monteiro, Andre, 2008. "The multi-state latent factor intensity model for credit rating transitions," Journal of Econometrics, Elsevier, vol. 142(1), pages 399-424, January.
    4. Fiona Steele & Ian Diamond & Duolao Wang, 1996. "The determinants of the duration of contraceptive use in China: A multilevel multinomial discrete-hazards mdeling approach," Demography, Springer;Population Association of America (PAA), vol. 33(1), pages 12-23, February.
    5. Robert A. Jarrow & David Lando & Stuart M. Turnbull, 2008. "A Markov Model for the Term Structure of Credit Risk Spreads," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 18, pages 411-453, World Scientific Publishing Co. Pte. Ltd..
    6. Enberg, John & Gottschalk, Peter & Wolf, Douglas, 1990. "A random-effects logit model of work-welfare transitions," Journal of Econometrics, Elsevier, vol. 43(1-2), pages 63-75.
    7. Samuli Ripatti & Juni Palmgren, 2000. "Estimation of Multivariate Frailty Models Using Penalized Partial Likelihood," Biometrics, The International Biometric Society, vol. 56(4), pages 1016-1022, December.
    8. Leow, Mindy & Crook, Jonathan, 2014. "Intensity models and transition probabilities for credit card loan delinquencies," European Journal of Operational Research, Elsevier, vol. 236(2), pages 685-694.
    9. T Bellotti & J Crook, 2009. "Credit scoring with macroeconomic variables using survival analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(12), pages 1699-1707, December.
    10. Patrick Gagliardini, 2005. "Stochastic Migration Models with Application to Corporate Risk," Journal of Financial Econometrics, Oxford University Press, vol. 3(2), pages 188-226.
    11. J Banasik & J N Crook & L C Thomas, 1999. "Not if but when will borrowers default," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(12), pages 1185-1190, December.
    12. Dario Gasbarra & S. R. Karia, 2000. "Analysis of Competing Risks by Using Bayesian Smoothing," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 27(4), pages 605-617, December.
    13. M Stepanova & L C Thomas, 2001. "PHAB scores: proportional hazards analysis behavioural scores," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(9), pages 1007-1016, September.
    14. Maria Stepanova & Lyn Thomas, 2002. "Survival Analysis Methods for Personal Loan Data," Operations Research, INFORMS, vol. 50(2), pages 277-289, April.
    15. Bellotti, Tony & Crook, Jonathan, 2013. "Forecasting and stress testing credit card default using dynamic models," International Journal of Forecasting, Elsevier, vol. 29(4), pages 563-574.
    16. Figlewski, Stephen & Frydman, Halina & Liang, Weijian, 2012. "Modeling the effect of macroeconomic factors on corporate default and credit rating transitions," International Review of Economics & Finance, Elsevier, vol. 21(1), pages 87-105.
    17. Lando, David & Skodeberg, Torben M., 2002. "Analyzing rating transitions and rating drift with continuous observations," Journal of Banking & Finance, Elsevier, vol. 26(2-3), pages 423-444, March.
    18. James Vaupel & Kenneth Manton & Eric Stallard, 1979. "The impact of heterogeneity in individual frailty on the dynamics of mortality," Demography, Springer;Population Association of America (PAA), vol. 16(3), pages 439-454, August.
    19. Bellotti, Tony & Crook, Jonathan, 2012. "Loss given default models incorporating macroeconomic variables for credit cards," International Journal of Forecasting, Elsevier, vol. 28(1), pages 171-182.
    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. 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.

    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. 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).
    2. Oliver Blümke, 2022. "Multiperiod default probability forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 677-696, July.
    3. Djeundje, Viani Biatat & Crook, Jonathan, 2019. "Dynamic survival models with varying coefficients for credit risks," European Journal of Operational Research, Elsevier, vol. 275(1), pages 319-333.
    4. 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.
    5. Leow, Mindy & Crook, Jonathan, 2014. "Intensity models and transition probabilities for credit card loan delinquencies," European Journal of Operational Research, Elsevier, vol. 236(2), pages 685-694.
    6. 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.
    7. Areski Cousin & Jérôme Lelong & Tom Picard, 2023. "Rating transitions forecasting: a filtering approach," Post-Print hal-03347521, HAL.
    8. repec:syb:wpbsba:03/2013 is not listed on IDEAS
    9. Areski Cousin & J'er^ome Lelong & Tom Picard, 2021. "Rating transitions forecasting: a filtering approach," Papers 2109.10567, arXiv.org, revised Jun 2023.
    10. 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.
    11. Areski Cousin & Jérôme Lelong & Tom Picard, 2022. "Rating transitions forecasting: a filtering approach," Working Papers hal-03347521, HAL.
    12. Areski Cousin & Mohamed Reda Kheliouen, 2016. "A comparative study on the estimation of factor migration models," Working Papers halshs-01351926, HAL.
    13. 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.
    14. Telg, Sean & Dubinova, Anna & Lucas, Andre, 2023. "Covid-19, credit risk management modeling, and government support," Journal of Banking & Finance, Elsevier, vol. 147(C).
    15. Luo, Sirong & Kong, Xiao & Nie, Tingting, 2016. "Spline based survival model for credit risk modeling," European Journal of Operational Research, Elsevier, vol. 253(3), pages 869-879.
    16. Malik, Madhur & Thomas, Lyn C., 2012. "Transition matrix models of consumer credit ratings," International Journal of Forecasting, Elsevier, vol. 28(1), pages 261-272.
    17. Figlewski, Stephen & Frydman, Halina & Liang, Weijian, 2012. "Modeling the effect of macroeconomic factors on corporate default and credit rating transitions," International Review of Economics & Finance, Elsevier, vol. 21(1), pages 87-105.
    18. Thi Mai Luong, 2020. "Selection Effects of Lender and Borrower Choices on Risk Measurement, Management and Prudential Regulation," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 3-2020.
    19. Miroslav Plasil & Tomas Konecny & Jakub Seidler & Petr Hlavac, 2015. "In the Quest of Measuring the Financial Cycle," Working Papers 2015/05, Czech National Bank.
    20. Sanchez-Barrios, Luis Javier & Andreeva, Galina & Ansell, Jake, 2016. "“Time-to-profit scorecards for revolving credit”," European Journal of Operational Research, Elsevier, vol. 249(2), pages 397-406.
    21. Tomáš Vaněk & David Hampel, 2017. "The Probability of Default Under IFRS 9: Multi-period Estimation and Macroeconomic Forecast," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 65(2), pages 759-776.

    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:eee:ejores:v:271:y:2018:i:2:p:697-709. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.