Optimal singular dividend control with capital injection and affine penalty payment at ruin

IF 0.7 3区 工程技术 Q4 ENGINEERING, INDUSTRIAL Probability in the Engineering and Informational Sciences Pub Date : 2022-08-12 DOI:10.1017/S0269964822000249
Ran Xu
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Abstract

In this paper, we extend the optimal dividend and capital injection problem with affine penalty at ruin in (Xu, R. & Woo, J.K. (2020). Insurance: Mathematics and Economics 92: 1–16) to the case with singular dividend payments. The asymptotic relationships between our value function to the one with bounded dividend density are studied, which also help to verify that our value function is a viscosity solution to the associated Hamilton–Jacob–Bellman Quasi-Variational Inequality (HJBQVI). We also show that the value function is the smallest viscosity supersolution within certain functional class. A modified comparison principle is proved to guarantee the uniqueness of the value function as the viscosity solution within the same functional class. Finally, a band-type dividend and capital injection strategy is constructed based on four crucial sets; and the optimality of such band-type strategy is proved by using fixed point argument. Numerical examples of the optimal band-type strategies are provided at the end when the claim size follows exponential and gamma distribution, respectively.
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具有资本注入和仿射罚金支付的最优奇异股利控制
在本文中,我们扩展了(Xu, R. & Woo, J.K.(2020))中具有仿射惩罚的最优股利和资本注入问题。保险:数学与经济92:1-16)的情况下的单一股息支付。研究了我们的值函数与有界红利密度的值函数之间的渐近关系,这也有助于验证我们的值函数是相关的hamilton - jack - bellman拟变分不等式(HJBQVI)的粘滞解。在一定的泛函类中,值函数是最小的黏度超解。证明了一种改进的比较原理,保证了值函数作为黏度解在同一函数类内的唯一性。最后,基于四个关键集合,构建了带式股利注资策略;并利用不动点论证证明了这种带型策略的最优性。最后给出了索赔规模分别服从指数分布和伽马分布时最优带型策略的数值算例。
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来源期刊
CiteScore
2.20
自引率
18.20%
发文量
45
审稿时长
>12 weeks
期刊介绍: The primary focus of the journal is on stochastic modelling in the physical and engineering sciences, with particular emphasis on queueing theory, reliability theory, inventory theory, simulation, mathematical finance and probabilistic networks and graphs. Papers on analytic properties and related disciplines are also considered, as well as more general papers on applied and computational probability, if appropriate. Readers include academics working in statistics, operations research, computer science, engineering, management science and physical sciences as well as industrial practitioners engaged in telecommunications, computer science, financial engineering, operations research and management science.
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