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Time varying and dynamic models for default risk in consumer loans

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  • Jonathan Crook
  • Tony Bellotti

Abstract

We review the incorporation of time varying variables into models of the risk of consumer default. Lenders typically have data which are of a panel format. This allows the inclusion of time varying covariates in models of account level default by including them in survival models, panel models or 'correction factor' models. The choice depends on the aim of the model and the assumptions that can be plausibly made. At the level of the portfolio, Merton-type models have incorporated macroeconomic and latent variables in mixed (factor) models and Kalman filter models whereas reduced form approaches include Markov chains and stochastic intensity models. The latter models have mainly been applied to corporate defaults and considerable scope remains for application to consumer loans. Copyright (c) 2009 Royal Statistical Society.

Suggested Citation

  • Jonathan Crook & Tony Bellotti, 2010. "Time varying and dynamic models for default risk in consumer loans," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(2), pages 283-305.
  • Handle: RePEc:bla:jorssa:v:173:y:2010:i:2:p:283-305
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Lee, Yongwoong & Poon, Ser-Huang, 2014. "Forecasting and decomposition of portfolio credit risk using macroeconomic and frailty factors," Journal of Economic Dynamics and Control, Elsevier, vol. 41(C), pages 69-92.
    2. Divino, Jose Angelo & Rocha, Líneke Clementino Sleegers, 2013. "Probability of default in collateralized credit operations," The North American Journal of Economics and Finance, Elsevier, vol. 25(C), pages 276-292.
    3. repec:wsi:ijtafx:v:20:y:2017:i:04:n:s0219024917500236 is not listed on IDEAS
    4. 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.
    5. Hon, Pak Shun & Bellotti, Tony, 2016. "Models and forecasts of credit card balance," European Journal of Operational Research, Elsevier, vol. 249(2), pages 498-505.
    6. Maria Rocha Sousa & João Gama & Elísio Brandão, 2013. "Introducing time-changing economics into credit scoring," FEP Working Papers 513, Universidade do Porto, Faculdade de Economia do Porto.
    7. Lee, Yongwoong & Rösch, Daniel & Scheule, Harald, 2016. "Accuracy of mortgage portfolio risk forecasts during financial crises," European Journal of Operational Research, Elsevier, vol. 249(2), pages 440-456.
    8. Allen, D.E. & Powell, R.J. & Singh, A.K., 2016. "Take it to the limit: Innovative CVaR applications to extreme credit risk measurement," European Journal of Operational Research, Elsevier, vol. 249(2), pages 465-475.
    9. 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.

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