Optimal insurance contract design with government disaster relief

IF 2.1 3区 经济学 Q2 BUSINESS, FINANCE
Sebastian Hinck
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Abstract

I examine the design of optimal insurance contracts considering the possibility of government disaster relief payments. This work focuses on the impact of (risky and ambiguous) government disaster relief on the shape of optimal private insurance contracts. I demonstrate that the optimal insurance contract is a straight deductible contract in the case of a fixed probability of government relief. This result is robust to ambiguity in the probability of relief payments, even for ambiguity-averse decision makers. If government disaster relief becomes more likely for larger losses, then the optimal insurance contract features coinsurance above a deductible. I also extend this analysis to more general stochastic dominance relationships between disaster relief and loss magnitude.

Abstract Image

考虑政府救灾的最优保险合同设计
我研究了最佳保险合同的设计,考虑到政府救灾付款的可能性。这项工作的重点是(风险和模糊的)政府救灾对最优私人保险合同形状的影响。我证明了在政府救济概率固定的情况下,最优保险合同是一个直接免赔合同。这一结果对救济金概率的模糊性是稳健的,即使对于厌恶模糊性的决策者也是如此。如果政府救灾更有可能造成更大的损失,那么最佳保险合同的特点是共同保险高于免赔额。我还将这一分析扩展到救灾和损失幅度之间更普遍的随机优势关系。
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来源期刊
CiteScore
3.50
自引率
15.80%
发文量
43
期刊介绍: The Journal of Risk and Insurance (JRI) is the premier outlet for theoretical and empirical research on the topics of insurance economics and risk management. Research in the JRI informs practice, policy-making, and regulation in insurance markets as well as corporate and household risk management. JRI is the flagship journal for the American Risk and Insurance Association, and is currently indexed by the American Economic Association’s Economic Literature Index, RePEc, the Social Sciences Citation Index, and others. Issues of the Journal of Risk and Insurance, from volume one to volume 82 (2015), are available online through JSTOR . Recent issues of JRI are available through Wiley Online Library. In addition to the research areas of traditional strength for the JRI, the editorial team highlights below specific areas for special focus in the near term, due to their current relevance for the field.
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