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Using quantile regression for rate-making

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  • Kudryavtsev, Andrey A.

Abstract

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.

Suggested Citation

  • Kudryavtsev, Andrey A., 2009. "Using quantile regression for rate-making," Insurance: Mathematics and Economics, Elsevier, vol. 45(2), pages 296-304, October.
    • Handle: RePEc:eee:insuma:v:45:y:2009:i:2:p:296-304
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    References listed on IDEAS

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    1. de Jong,Piet & Heller,Gillian Z., 2008. "Generalized Linear Models for Insurance Data," Cambridge Books, Cambridge University Press, number 9780521879149, November.
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    Cited by:

    1. Wei Wang & Limin Wen & Zhixin Yang & Quan Yuan, 2020. "Quantile Credibility Models with Common Effects," Risks, MDPI, vol. 8(4), pages 1-10, September.
    2. Liang Yang & Zhengxiao Li & Shengwang Meng, 2020. "Risk Loadings in Classification Ratemaking," Papers 2002.01798, arXiv.org, revised Jan 2022.
    3. Fuzi, Mohd Fadzli Mohd & Jemain, Abdul Aziz & Ismail, Noriszura, 2016. "Bayesian quantile regression model for claim count data," Insurance: Mathematics and Economics, Elsevier, vol. 66(C), pages 124-137.
    4. Pitselis, Georgios, 2017. "Risk measures in a quantile regression credibility framework with Fama/French data applications," Insurance: Mathematics and Economics, Elsevier, vol. 74(C), pages 122-134.
    5. Richardson, Robert & Hartman, Brian, 2018. "Bayesian nonparametric regression models for modeling and predicting healthcare claims," Insurance: Mathematics and Economics, Elsevier, vol. 83(C), pages 1-8.
    6. Pitselis, Georgios, 2013. "Quantile credibility models," Insurance: Mathematics and Economics, Elsevier, vol. 52(3), pages 477-489.
    7. Pitselis, Georgios, 2020. "Multi-stage nested classification credibility quantile regression model," Insurance: Mathematics and Economics, Elsevier, vol. 92(C), pages 162-176.
    8. Gao, Suhao & Yu, Zhen, 2023. "Parametric expectile regression and its application for premium calculation," Insurance: Mathematics and Economics, Elsevier, vol. 111(C), pages 242-256.
    9. Li, Yinhuan & Fung, Tsz Chai & Peng, Liang & Qian, Linyi, 2023. "Diagnostic tests before modeling longitudinal actuarial data," Insurance: Mathematics and Economics, Elsevier, vol. 113(C), pages 310-325.
    10. Kang, Seul Ki & Peng, Liang & Xiao, Hongmin, 2020. "Risk analysis with categorical explanatory variables," Insurance: Mathematics and Economics, Elsevier, vol. 91(C), pages 238-243.

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