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Insurance ratemaking using a copula-based multivariate Tweedie model

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  • Peng Shi

Abstract

The Tweedie distribution, featured with a mass probability at zero, is a convenient tool for insurance claims modeling and pure premium determination in general insurance. Motivated by the fact that an insurance policy typically provides multiple types of coverage, we propose a copula-based multivariate Tweedie regression for modeling the semi-continuous claims while accommodating the association among different types. The proposed approach also allows for dispersion modeling, resulting in a multivariate version of the double generalized linear model. We demonstrate the application in insurance ratemaking using a portfolio of policyholders of automobile insurance from the state of Massachusetts in the United States.

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  • Peng Shi, 2016. "Insurance ratemaking using a copula-based multivariate Tweedie model," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2016(3), pages 198-215, March.
    • Handle: RePEc:taf:sactxx:v:2016:y:2016:i:3:p:198-215
    DOI: 10.1080/03461238.2014.921639
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    Cited by:

    1. Zifeng Zhao & Peng Shi & Xiaoping Feng, 2021. "Knowledge Learning of Insurance Risks Using Dependence Models," INFORMS Journal on Computing, INFORMS, vol. 33(3), pages 1177-1196, July.

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