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Maximizing expected terminal utility of an insurer with high gain tax by investment and reinsurance

机译:通过投资和再保险最大化具有高利得税的保险公司的预期终端效用

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This paper investigates optimal investment and proportional reinsurance policies for an insurer who subjects to pay high gain tax. The surplus process of the insurer and the return process of the financial market are both modulated by the external macroeconomic environment. The dynamic of the external macroeconomic environment is specified by a Markov chain with finite states. Once the insurer's accumulated profits attain a new maximum, they have to pay high gain tax. The objective of the insurer is to maximize the expected terminal utility by investment and reinsurance. The controlled wealth process of the insurer turned out to be a controlled jump diffusion process with reflections and Markov regime switching. By the weak dynamic programming principle (WDPP), we prove that the value function is the unique viscosity solution to the coupled Hamilton-Jacob-Bellman (HJB) equations with first derivative boundary constraints. By the Markov chain approximating method for the HJB equations, we construct a numerical scheme for approximating the viscosity solution to the coupled HJB equations. Two numerical examples are presented to illustrate the impact of both high gain tax and regime switching on the optimal policies. (C) 2019 Elsevier Ltd. All rights reserved.
机译:本文研究了需要缴纳高增值税的保险公司的最优投资和比例再保险政策。保险人的盈余过程和金融市场的回报过程均受外部宏观经济环境的调节。外部宏观经济环境的动态由具有有限状态的马尔可夫链指定。一旦保险公司的累计利润达到新的最高水平,他们就必须缴纳高额的增值税。保险公司的目标是通过投资和再保险来最大化预期的终端效用。保险公司的受控财富过程原来是具有反射和马尔可夫制度转换的受控跳跃扩散过程。通过弱动态规划原理(WDPP),我们证明了值函数是具有一阶导数边界约束的耦合Hamilton-Jacob-Bellman(HJB)方程的唯一粘性解。通过HJB方程的马尔可夫链近似方法,我们构建了一个数值模型,用于近似求解耦合的HJB方程的粘度解。给出了两个数值示例,以说明高利得税和政权转换对最优政策的影响。 (C)2019 Elsevier Ltd.保留所有权利。

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