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Spline based survival model for credit risk modeling

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  • Luo, Sirong
  • Kong, Xiao
  • Nie, Tingting

Abstract

Survival modeling has been adapted in retail banking because of its capability to analyze the censored data. It is an important tool for credit risk scoring, stress testing and credit asset evaluation. In this paper, we introduce a regression spline based discrete time survival model. The flexibility of spline function allows us to model the nonlinear and irregular shape of the hazard functions. By incorporating the regression spline into the multinomial logistic regression, this approach complements the existing Cox model. From a practical perspective, the logistic regression is relatively easy to understand and implement, and the simple parametric form is especially advantageous for predictive scoring. Using a credit card dataset, we demonstrate how to build a cubic regression spline based survival model. We also compare the performance of spline based discrete time survival model with the classical Cox model, our results show the spline based survival model can provide similar statistical explanatory and improve the prediction accuracy for attrition model which has low event rate.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:253:y:2016:i:3:p:869-879
    DOI: 10.1016/j.ejor.2016.02.050
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    References listed on IDEAS

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    1. David B. Gross, 2002. "An Empirical Analysis of Personal Bankruptcy and Delinquency," Review of Financial Studies, Society for Financial Studies, vol. 15(1), pages 319-347, March.
    2. 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.
    3. Poletti Laurini, Márcio & Moura, Marcelo, 2010. "Constrained smoothing B-splines for the term structure of interest rates," Insurance: Mathematics and Economics, Elsevier, vol. 46(2), pages 339-350, April.
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    5. Maria Stepanova & Lyn Thomas, 2002. "Survival Analysis Methods for Personal Loan Data," Operations Research, INFORMS, vol. 50(2), pages 277-289, April.
    6. 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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    Cited by:

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    5. 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.

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