Using quantile regression for rate-making
Regression models are popular tools for rate-making in the framework of heterogeneous insurance portfolios; however, the traditional regression methods have some disadvantages particularly their sensitivity to the assumptions which significantly restrict the area of their applications. This paper is devoted to an alternative approach-quantile regression. It is free of some disadvantages of the traditional models. The quality of estimators for the approach described is approximately the same as or sometimes better than that for the traditional regression methods. Moreover, the quantile regression is consistent with the idea of using the distribution quantile for rate-making. This paper provides detailed comparisons between the approaches and it gives the practical example of using the new methodology.
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- Roger Koenker & Kevin F. Hallock, 2001.
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- Rousseeuw, P. & Daniels, B. & Leroy, A., 1984. "Applying robust regression to insurance," Insurance: Mathematics and Economics, Elsevier, vol. 3(1), pages 67-72, January.
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- de Jong,Piet & Heller,Gillian Z., 2008. "Generalized Linear Models for Insurance Data," Cambridge Books, Cambridge University Press, number 9780521879149, December.
- Robert F. Engle & Simone Manganelli, 1999. "CAViaR: Conditional Value at Risk by Quantile Regression," NBER Working Papers 7341, National Bureau of Economic Research, Inc.
- Pitt, D. G. W., 2006. "Regression Quantile Analysis of Claim Termination Rates for Income Protection Insurance," Annals of Actuarial Science, Cambridge University Press, vol. 1(02), pages 345-357, September.
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