Optimal dividend policy with liability constraint under a hidden Markov regime-switching model

IF 1.2 4区 工程技术 Q3 ENGINEERING, MULTIDISCIPLINARY
Jiaqin Wei, Z. Jin, Hailiang Yang
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引用次数: 2

Abstract

This paper deals with the optimal liability and dividend strategies for an insurance company in Markov regime-switching models. The objective is to maximize the total expected discounted utility of dividend payment in the infinite time horizon in the logarithm and power utility cases, respectively. The switching process, which is interpreted by a hidden Markov chain, is not completely observable. By using the technique of the Wonham filter, the partially observed system is converted to a completely observed one and the necessary information is recovered. The upper-lower solution method is used to show the existence of classical solution of the associated second-order nonlinear Hamilton-Jacobi-Bellman equation in the two-regime case. The explicit solution of the value function is derived and the corresponding optimal dividend policies and liability ratios are obtained. In the multi-regime case, a general setting of the Wonham filter is presented, and the value function is proved to be a viscosity solution of the associated system of Hamilton-Jacobi-Bellman equations.
隐马尔可夫状态切换模型下具有负债约束的最优股利政策
本文研究了马尔可夫制度转换模型下保险公司的最优负债和股利策略。目标是在对数和电力效用情况下,在无限时间范围内最大化股息支付的总期望贴现效用。用隐马尔可夫链解释的转换过程是不完全可见的。利用Wonham滤波技术,将部分观测到的系统转换为完全观测到的系统,恢复必要的信息。利用上下解法证明了二阶非线性Hamilton-Jacobi-Bellman方程在两域情况下经典解的存在性。导出了价值函数的显式解,得到了相应的最优股利政策和负债比率。在多域情况下,给出了Wonham滤波器的一般设置,并证明了其值函数是Hamilton-Jacobi-Bellman方程关联系统的黏性解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
2.50
自引率
15.40%
发文量
207
审稿时长
18 months
期刊介绍: JIMO is an international journal devoted to publishing peer-reviewed, high quality, original papers on the non-trivial interplay between numerical optimization methods and practically significant problems in industry or management so as to achieve superior design, planning and/or operation. Its objective is to promote collaboration between optimization specialists, industrial practitioners and management scientists so that important practical industrial and management problems can be addressed by the use of appropriate, recent advanced optimization techniques.
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