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Performance monitoring of credit portfolios using survival analysis

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  • Gandy, Axel

Abstract

We are interested in detecting changes in the performance of a credit portfolio quickly and robustly. The portfolio is dynamic: customers can either default or pay the full amount, and new customers can be taken on. Robust detection means that changing the number of new customers taken on should not lead to either a false or delayed signal. We investigate the performances of monitoring schemes via a simulation study that uses several scenarios. We consider monitoring based on default rates estimated through a gliding window, cumulative sum (CUSUM) charts based on default rates, CUSUM charts based on defaults within a given follow-up time after arrival, and a survival analysis CUSUM chart. We conclude that using a survival analysis approach is preferable to using the other approaches.

Suggested Citation

  • Gandy, Axel, 2012. "Performance monitoring of credit portfolios using survival analysis," International Journal of Forecasting, Elsevier, vol. 28(1), pages 139-144.
    • Handle: RePEc:eee:intfor:v:28:y:2012:i:1:p:139-144
    DOI: 10.1016/j.ijforecast.2010.08.006
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Maria Stepanova & Lyn Thomas, 2002. "Survival Analysis Methods for Personal Loan Data," Operations Research, INFORMS, vol. 50(2), pages 277-289, April.
    4. A. Gandy & J. T. Kvaløy & A. Bottle & F. Zhou, 2010. "Risk-adjusted monitoring of time to event," Biometrika, Biometrika Trust, vol. 97(2), pages 375-388.
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    2. Dumičić Ksenija & Žmuk Berislav, 2015. "Statistical Control Charts: Performances of Short Term Stock Trading in Croatia," Business Systems Research, Sciendo, vol. 6(1), pages 22-35, March.

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