Optimal investment-withdrawal strategy for variable annuities under a performance fee structure

IF 1.9 3区 经济学 Q2 ECONOMICS
Runhuan Feng , Xiaochen Jing , Kenneth Tsz Hin Ng
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引用次数: 0

Abstract

Variable Annuities (VAs) provide policyholders with market participation while offering additional protection from insurers. In this article, we develop a mathematical model to explore the impact of different fee structures on VAs with a ratchet feature and derive analytical solutions to the associated optimal investment-withdrawal problem. We focus on a performance fee structure, highlighting its advantages over the traditional constant fee structure from both the insurer's and policyholder's perspectives. Our findings show that policyholders adopt more conservative investment strategies under the performance fee, leading to increased expected profits and reduced tail risks for risk-neutral insurers. From a mathematical standpoint, we contribute by proving the well-posedness of the associated free-boundary value problems (FBPs) and establishing verification theorems for the underlying control problems. These results involve non-standard analysis and estimations due to the ratchet feature and the guaranteed protections embedded in the contract.
绩效费结构下变额年金的最优投资-提取策略
变额年金(Variable Annuities,VA)在为投保人提供市场参与的同时,也为其提供了来自保险公司的额外保障。在本文中,我们建立了一个数学模型来探讨不同费用结构对具有棘轮特征的变额年金的影响,并推导出相关最优投资-提取问题的分析解决方案。我们重点研究了绩效费用结构,从保险公司和投保人的角度强调了其相对于传统恒定费用结构的优势。我们的研究结果表明,投保人在业绩费用下会采取更保守的投资策略,从而增加预期利润,降低风险中性的保险公司的尾部风险。从数学角度来看,我们证明了相关自由边界值问题(FBPs)的良好可求性,并建立了相关控制问题的验证定理。这些结果涉及非标准分析和估算,原因是合同中嵌入了棘轮特征和保证保护。
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
3.10
自引率
10.50%
发文量
199
期刊介绍: The journal provides an outlet for publication of research concerning all theoretical and empirical aspects of economic dynamics and control as well as the development and use of computational methods in economics and finance. Contributions regarding computational methods may include, but are not restricted to, artificial intelligence, databases, decision support systems, genetic algorithms, modelling languages, neural networks, numerical algorithms for optimization, control and equilibria, parallel computing and qualitative reasoning.
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