HLSV 模型下最优投资、消费和人寿保险策略的 Legendre 变换二元渐近解

Jianyu Huo, Qing Zhou
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引用次数: 0

摘要

我们研究了一个连续两代人(即父母和子女)的家庭在投资、消费和购买人寿保险方面的最优决策。父母可以投资于无风险资产和风险资产,其中风险资产的价格由赫斯顿局部随机波动模型驱动,能更好地反映市场状况。可以购买人寿保险,以规避父母在退休前意外去世造成的财富损失,尤其是在子女没有收入的情况下。同时,父母和子女的效用函数与不确定的寿命有关。家庭的目标是适当地最大化父母和子女各自效用的加权平均值。为了得出最优策略,我们采用了对偶法、勒让德变换和渐近展开技术,通过动态编程方法求解相关的汉密尔顿-贾可比-贝尔曼方程。最后,我们得到了渐近解,并提供了数值示例来说明一些重要参数对最优策略的影响。
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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Legendre transform dual-asymptotic solution for optimal investment, consumption and life insurance strategy under the HLSV model

We investigate the optimal decisions on investment, consumption and purchasing life insurance of a household with two consecutive generations, say parents and children. Parents can invest in risk-free and risky assets, with the risky asset’s price driven by the Heston local-stochastic volatility model, better reflecting market conditions. Life insurance can be purchased to hedge against wealth loss from parents’ unexpected death before retirement, especially if children have no income. Meanwhile, utility functions of the parents and children are individually considered in relation to the uncertain lifetime. The objective of the household is to appropriately maximize the weighted average of the respective utilities of parents and children. In order to derive the optimal strategies, we adopt a dual method, Legendre transformation, and an asymptotic expansion technique to solve the associated Hamilton–Jacobi–Bellman equation achieved by a dynamic programming approach. Finally, an asymptotic solution is obtained and numerical examples are provided to illustrate the impacts of some important parameters on the optimal strategies.

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11.50%
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352
期刊介绍: Computational & Applied Mathematics began to be published in 1981. This journal was conceived as the main scientific publication of SBMAC (Brazilian Society of Computational and Applied Mathematics). The objective of the journal is the publication of original research in Applied and Computational Mathematics, with interfaces in Physics, Engineering, Chemistry, Biology, Operations Research, Statistics, Social Sciences and Economy. The journal has the usual quality standards of scientific international journals and we aim high level of contributions in terms of originality, depth and relevance.
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