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<p>In the past years, climate change has affected honey production more and more and the reduction has become a significant risk for beekeepers. In this paper, we discuss the pricing of a parametric insurance policy drafted to cover the potential losses in terms of honey production due to unfavorable weather conditions: the payment of the insurance benefit is triggered by the breaching of predefined thresholds of a weather index, measured over specific relevant periods. The effectiveness of the coverage is verified by the means of random forests, where the honey production is forecast under different real-world weather scenarios and the beekeepers’ loss is compared with the insurance benefit reimbursed (or not) by the policy. The random forest technique is put along with more common ones, such as ordinary least squares regression and mixed linear models. A practical example is given for the Italian market, where the pricing is derived and assessed for three different zones: North, Centre, and South.</p>

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A parametric insurance policy for beekeepers and honey production: random forest regressions and real-world pricing

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月21日

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  • Ilaria Colivicchi,
  • Silvia Dell’Acqua,
  • Vincenzo Russo

摘要

In the past years, climate change has affected honey production more and more and the reduction has become a significant risk for beekeepers. In this paper, we discuss the pricing of a parametric insurance policy drafted to cover the potential losses in terms of honey production due to unfavorable weather conditions: the payment of the insurance benefit is triggered by the breaching of predefined thresholds of a weather index, measured over specific relevant periods. The effectiveness of the coverage is verified by the means of random forests, where the honey production is forecast under different real-world weather scenarios and the beekeepers’ loss is compared with the insurance benefit reimbursed (or not) by the policy. The random forest technique is put along with more common ones, such as ordinary least squares regression and mixed linear models. A practical example is given for the Italian market, where the pricing is derived and assessed for three different zones: North, Centre, and South.