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Assessing pricing assumptions for weather index insurance in a changing climate

机译:在变化的气候中评估天气指数保险的定价假设

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摘要

Abstract Weather index insurance is being offered to low-income farmers in developing countries as an alternative to traditional multi-peril crop insurance. There is widespread support for index insurance as a means of climate change adaptation but whether or not these products are themselves resilient to climate change has not been well studied. Given climate variability and climate change, an over-reliance on historical climate observations to guide the design of such products can result in premiums which mislead policyholders and insurers alike, about the magnitude of underlying risks. Here, a method to incorporate different sources of climate data into the product design phase is presented. Bayesian Networks are constructed to demonstrate how insurers can assess the product viability from a climate perspective, using past observations and simulations of future climate. Sensitivity analyses illustrate the dependence of pricing decisions on both the choice of information, and the method for incorporating such data. The methods and their sensitivities are illustrated using a case study analysing the provision of index-based crop insurance in Kolhapur, India. We expose the benefits and limitations of the Bayesian Network approach, weather index insurance as an adaptation measure and climate simulations as a source of quantitative predictive information. Current climate model output is shown to be of limited value and difficult to use by index insurance practitioners. The method presented, however, is shown to be an effective tool for testing pricing assumptions and could feasibly be employed in the future to incorporate multiple sources of climate data.
机译:摘要气象指数保险正在向发展中国家的低收入农民提供,以替代传统的多灾农作物保险。人们广泛支持指数保险作为适应气候变化的一种手段,但是这些产品本身是否具有抵御气候变化的能力尚未得到很好的研究。鉴于气候变化和气候变化,过度依赖历史气候观察来指导此类产品的设计可能导致溢价,从而误导保单持有人和保险公司等潜在风险的规模。在这里,提出了一种将不同气候数据源纳入产品设计阶段的方法。贝叶斯网络的构建旨在通过过去的观察和对未来气候的模拟,展示保险公司如何从气候角度评估产品生存能力。敏感性分析说明了定价决策对信息选择和合并此类数据的方法的依赖性。案例分析了印度Kolhapur提供基于指数的农作物保险的案例研究,说明了这些方法及其敏感性。我们展示了贝叶斯网络方法的好处和局限性,天气指数保险作为一种适应措施以及气候模拟作为定量预测信息的来源。当前的气候模型输出显示出有限的价值,并且难以被指数保险从业者使用。但是,所展示的方法被证明是检验定价假设的有效工具,并且在将来可能可行地用于合并多种气候数据源。

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