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Agricultural Insurance Ratemaking: Development of a New Premium Principle

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  • Wenjun Zhu
  • Ken Seng Tan
  • Lysa Porth

Abstract

Determining the appropriate premium to charge for the underlying risk is central to delivering a sustainable agricultural insurance program. Though this is fundamental to all types of insurance, in agriculture this is a particularly challenging task given systemic risk, information asymmetry, and a number of multifaceted factors pertaining to the loss experience data, including scarcity and credibility. The objective of this article is to formally introduce premium principles to the agricultural insurance literature, with a focus on a new premium principle approach based on the multivariate weighted distribution. The multivariate weighted premium principle (MWPP) formalizes the reweighting of historical loss experience using auxiliary factors in order to refine the agricultural insurance pricing. These auxiliary factors may reflect systemic risk and include material information, such as economic and market conditions, weather, soil, etc. In the empirical study, a unique reinsurance data set from the province of Manitoba, Canada, is used to evaluate a number of potential premium principles. With the flexibility of the MWPP, the empirical results indicate that the MWPP approach can be a viable premium principle for pricing agricultural insurance. Furthermore, the MWPP redistributes premium rates and assigns increased loadings to higher risk layers, helping reinsurers manage their reserves and achieve improved sustainability in the long term.

Suggested Citation

  • Wenjun Zhu & Ken Seng Tan & Lysa Porth, 2019. "Agricultural Insurance Ratemaking: Development of a New Premium Principle," North American Actuarial Journal, Taylor & Francis Journals, vol. 23(4), pages 512-534, October.
  • Handle: RePEc:taf:uaajxx:v:23:y:2019:i:4:p:512-534
    DOI: 10.1080/10920277.2019.1618340
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    Cited by:

    1. Shaolong Zeng & Bingying Qi & Minglin Wang, 2022. "Agricultural Insurance and Agricultural Economic Growth: The Case of Zhejiang Province in China," IJERPH, MDPI, vol. 19(20), pages 1-21, October.
    2. Eric C. K. Cheung & Oscar Peralta & Jae-Kyung Woo, 2021. "Multivariate matrix-exponential affine mixtures and their applications in risk theory," Papers 2201.11122, arXiv.org.
    3. Cheung, Eric C.K. & Peralta, Oscar & Woo, Jae-Kyung, 2022. "Multivariate matrix-exponential affine mixtures and their applications in risk theory," Insurance: Mathematics and Economics, Elsevier, vol. 106(C), pages 364-389.
    4. Holly Brannelly & Andrea Macrina & Gareth W. Peters, 2021. "Stochastic measure distortions induced by quantile processes for risk quantification and valuation," Papers 2201.02045, arXiv.org.

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