Three-step risk inference in insurance ratemaking

Yanxi Hou, Seul Ki Kang, Chia Chun Lo and Liang Peng

Insurance: Mathematics and Economics, 2022, vol. 105, issue C, 1-13

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.

Keywords: Generalized Pareto distribution; Insurance loss; Logistic regression; Quantile regression; Random weighted bootstrap (search for similar items in EconPapers)
JEL-codes: C10 G22 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:insuma:v:105:y:2022:i:c:p:1-13

DOI: 10.1016/j.insmatheco.2022.03.005

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