信念依赖效用和模糊性下的最优保险设计

IF 0.9 3区 经济学 Q3 BUSINESS, FINANCE Mathematics and Financial Economics Pub Date : 2023-11-20 DOI:10.1007/s11579-023-00349-5
Yulian Fan
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引用次数: 0

摘要

我们通过Fan提出的信念依赖效用(BDU)引入了模糊情况下的平滑决策模型(数学学报,应用,37(4):682-696,2021)。利用BDU下的光滑决策模型,得到保险人的明确最优保险策略。然后研究了保费约束下(假设保险人为风险中性)被保险人的最优保险策略。明确的结果可以解释一些与经典保险契约文献不一致的保险需求显著行为。例如,如果被保险人对小损失非常敏感,而保险人对小损失不那么敏感(或者保险人是风险中性的),则被保险人会更倾向于购买小损失保证,而不是购买针对毁灭性损失的保护,这与保险市场上观察到的一些保险需求行为是一致的。如果被保险人对小损失不像保险人那样敏感,那么针对超过免赔额的大损失的保险单将会受到欢迎。最后,本文给出了一个实例。
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Optimal insurance design under belief-dependent utility and ambiguity

We introduce a smooth decision model under ambiguity by the belief-dependent utility (BDU) proposed in Fan (Acta Math Appl Sin 37(4):682–696, 2021). Using the smooth decision model under BDU, we get the explicit optimal insurance policy for the insurer. Then the optimal insurance policy for the insured under premium constraint (the insurer is assumed to be risk neutral) is studied. The explicit results can explain some notable behaviors in insurance demand which are inconsistent with the classical insurance contracting literature. For example, if the insured is very sensitive to small losses and the insurer is not so sensitive to small losses (or the insurer is risk neutral), the insured will prefer to purchase warranties for small losses rather than buy protections against devastating losses, which is consistent with some insurance demand behaviors observed on the insurance market. If the insured is less sensitive to small losses than the insurer, insurance policy against large losses above a deductible will be popular. At last, this paper provides an example.

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来源期刊
Mathematics and Financial Economics
Mathematics and Financial Economics MATHEMATICS, INTERDISCIPLINARY APPLICATIONS -
CiteScore
2.80
自引率
6.20%
发文量
17
期刊介绍: The primary objective of the journal is to provide a forum for work in finance which expresses economic ideas using formal mathematical reasoning. The work should have real economic content and the mathematical reasoning should be new and correct.
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