基于衍生品的最优投资与比例再保险策略研究

IF 1.2 4区 工程技术 Q3 ENGINEERING, MULTIDISCIPLINARY
Jun Feng, Shaoyong Lai, Liting Zhou
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引用次数: 0

摘要

研究了具有随机波动和跳跃风险的最优衍生品投资和比例再保险问题。假设保险人具有恒定的绝对风险厌恶偏好。保险人通过购买比例再保险转移部分保险风险,并将盈余以无风险债券、随机波动率跳跃扩散模型描述的风险股票和两个非冗余衍生品投资于金融市场。对有衍生品交易的再保险和投资最优策略进行了封闭式分析,对无衍生品交易的再保险和投资最优策略进行了数值分析。用确定性-等价的方法分析了衍生品交易的收益。我们的研究结果表明,与金融市场的头寸规模相比,衍生品交易的价值始终是积极的和可观的。此外,在金融市场风险独立于保险市场风险的假设下,再保险的最优策略独立于投资策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Investigations to the optimal derivative-based investment and proportional reinsurance strategies
The optimal derivative-based investment and proportional reinsurance problems with stochastic volatility and jump risks are investigated. Assume that insurer possesses the constant absolute risk aversion preference. The insurer transfers part of insurance risk via purchasing proportional reinsurance and invests surplus in financial market with a risk-free bond, a risky stock described by the stochastic volatility jump diffusion model, and two non-redundant derivatives. The optimal strategies of reinsurance and investment with derivative trading are obtained in closed-form, while the optimal strategies with no derivative trading are numerically analyzed. The gain from derivative trading is analyzed by the method of certainty-equivalence. Our results illustrate that the value of derivative trading is always positive and sizeable, compared with the size of positions in the financial market. Moreover, under the assumption that the risks in the financial market are independent of those in the insurance market, the optimal strategy of reinsurance is independent of that of investment.
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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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