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A two-stage model for high-risk prediction in insurance ratemaking: Asymptotics and inference

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  • Hou, Yanxi

Abstract

In actuarial practice, modern statistical methodologies are one primary consideration for real actuarial problems, such as premium calculation, insurance preservation, marginal risk analysis, etc. The claim data usually possesses a complex data structure, so direct applications of statistical techniques will result in unstable prediction. For example, insurance losses are semicontinuous variables, where a positive mass on zero is often associated with an otherwise positive continuous outcome. Thus, the prediction of high-risk events of claim data needs additional treatment to avoid significant underestimation. In this article, we propose a new two-stage composite quantile regression model for the prediction of the value-at-risks of the aggregate insurance losses. As we are interested in the statistical properties of our method, the asymptotic results are established corresponding to different types of risk levels. Finally, some simulation studies and a data analysis are implemented for the illustration of our method.

Suggested Citation

  • Hou, Yanxi, 2022. "A two-stage model for high-risk prediction in insurance ratemaking: Asymptotics and inference," Insurance: Mathematics and Economics, Elsevier, vol. 104(C), pages 283-301.
  • Handle: RePEc:eee:insuma:v:104:y:2022:i:c:p:283-301
    DOI: 10.1016/j.insmatheco.2022.03.003
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    Citations

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    Cited by:

    1. Syuhada, Khreshna & Tjahjono, Venansius & Hakim, Arief, 2024. "Compound Poisson–Lindley process with Sarmanov dependence structure and its application for premium-based spectral risk forecasting," Applied Mathematics and Computation, Elsevier, vol. 467(C).
    2. 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.

    More about this item

    Keywords

    Insurance claim; Value at risk; Extreme value theory; Composite quantile regression; Two-stage inference;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies

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