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Multiperil rate making for property insurance using longitudinal data

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  • Lu Yang
  • Peng Shi

Abstract

In property insurance, a contract often provides the policyholder with protection against damages to the insured properties that arise from a variety of perils. We propose a multivariate framework for pricing property insurance contracts with multiperil coverage in a longitudinal context. Specifically, a two‐part model is employed to accommodate the excess of 0s and heavy tails in the insurance loss cost, and a Gaussian copula with a structured correlation is used to capture the dependence within and between perils, as well as their interaction. Using the government property insurance data from the state of Wisconsin in the USA, we show that the multiperil claim model has important implications in both experience rating and risk margin analysis.

Suggested Citation

  • Lu Yang & Peng Shi, 2019. "Multiperil rate making for property insurance using longitudinal data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(2), pages 647-668, February.
    • Handle: RePEc:bla:jorssa:v:182:y:2019:i:2:p:647-668
    DOI: 10.1111/rssa.12419
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    Cited by:

    1. Verschuren, Robert Matthijs, 2022. "Frequency-severity experience rating based on latent Markovian risk profiles," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 379-392.
    2. Peng Shi & Glenn M. Fung & Daniel Dickinson, 2022. "Assessing hail risk for property insurers with a dependent marked point process," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 302-328, January.
    3. Bladt, Martin & Yslas, Jorge, 2023. "Robust claim frequency modeling through phase-type mixture-of-experts regression," Insurance: Mathematics and Economics, Elsevier, vol. 111(C), pages 1-22.
    4. Gao, Lisa & Shi, Peng, 2022. "Leveraging high-resolution weather information to predict hail damage claims: A spatial point process for replicated point patterns," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 161-179.
    5. 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.
    6. Denuit, Michel & Lu, Yang, 2020. "Wishart-Gamma mixtures for multiperil experience ratemaking, frequency-severity experience rating and micro-loss reserving," LIDAM Discussion Papers ISBA 2020016, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    7. Michel Denuit & Yang Lu, 2021. "Wishart‐gamma random effects models with applications to nonlife insurance," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 88(2), pages 443-481, June.

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