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Three-step risk inference in insurance ratemaking

Author

Listed:
  • Hou, Yanxi
  • Kang, Seul Ki
  • Lo, Chia Chun
  • Peng, Liang

Abstract

As catastrophic events happen more and more frequently, accurately forecasting risk at a high level is vital for the financial stability of the insurance industry. This paper proposes an efficient three-step procedure to deal with the semicontinuous property of insurance claim data and forecast extreme risk. The first step uses a logistic regression model to estimate the nonzero claim probability. The second step employs a quantile regression model to select a dynamic threshold for fitting the loss distribution semiparametrically. The third step fits a generalized Pareto distribution to exceedances over the selected dynamic threshold. Combining these three steps leads to an efficient risk forecast. Furthermore, a random weighted bootstrap method is employed to quantify the uncertainty of the derived risk forecast. Finally, we apply the proposed method to an automobile insurance data set.

Suggested Citation

  • Hou, Yanxi & Kang, Seul Ki & Lo, Chia Chun & Peng, Liang, 2022. "Three-step risk inference in insurance ratemaking," Insurance: Mathematics and Economics, Elsevier, vol. 105(C), pages 1-13.
    • Handle: RePEc:eee:insuma:v:105:y:2022:i:c:p:1-13
    DOI: 10.1016/j.insmatheco.2022.03.005
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    Citations

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    Cited by:

    1. 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

    Generalized Pareto distribution; Insurance loss; Logistic regression; Quantile regression; Random weighted bootstrap;
    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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