随机波动下两个均值方差保险公司之间的一类非零和随机微分对策

IF 0.7 3区 工程技术 Q4 ENGINEERING, INDUSTRIAL
Jiannan Zhang, Ping Chen, Z. Jin, Shuanming Li
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引用次数: 0

摘要

本文研究了不完全市场中具有状态依赖平均期望的两保险人之间的一类非零和再保险投资随机微分对策的开环均衡策略。两家保险公司都可以购买比例再保险合同,并将其财富投资于无风险资产和风险资产,其价格由一般随机波动模型建模。两个保险公司的盈余过程由两个标准布朗运动驱动。每个保险公司的目标是找到平衡的投资和再保险策略,以平衡预期收益和相对终端财富的方差。结合正倒向随机微分方程,导出了两保险公司均衡控制的充分条件和一般解。此外,我们将理论结果应用于两种特殊的随机波动模型(Hull-White模型和Heston模型)。数值算例也说明了我们的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A class of non-zero-sum stochastic differential games between two mean–variance insurers under stochastic volatility
This paper studies the open-loop equilibrium strategies for a class of non-zero-sum reinsurance–investment stochastic differential games between two insurers with a state-dependent mean expectation in the incomplete market. Both insurers are able to purchase proportional reinsurance contracts and invest their wealth in a risk-free asset and a risky asset whose price is modeled by a general stochastic volatility model. The surplus processes of two insurers are driven by two standard Brownian motions. The objective for each insurer is to find the equilibrium investment and reinsurance strategies to balance the expected return and variance of relative terminal wealth. Incorporating the forward backward stochastic differential equations (FBSDEs), we derive the sufficient conditions and obtain the general solutions of equilibrium controls for two insurers. Furthermore, we apply our theoretical results to two special stochastic volatility models (Hull–White model and Heston model). Numerical examples are also provided to illustrate our results.
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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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