具有独立随机劳动收入的最优消费与投资

IF 1.4 3区 数学 Q2 MATHEMATICS, APPLIED Mathematics of Operations Research Pub Date : 2024-03-05 DOI:10.1287/moor.2023.0119
Alain Bensoussan, Seyoung Park
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引用次数: 0

摘要

我们建立了一个新的动态连续时间最优消费和投资模型,其中包括独立的随机劳动收入。我们将贝尔曼方程的求解问题简化为积分方程的求解问题。然后,我们将最优消费和投资策略明确表征为收入与财富比率的函数。我们提供了一些与价值函数和最优策略相关的分析比较静态。我们还开发了一种相当通用的控制迭代数值算法,并将贝尔曼方程作为常微分方程的解序列来求解。这种数值算法可以很容易地应用于许多其他最优消费和投资问题,尤其是涉及额外的不可分散布朗风险,从而导致非线性贝尔曼方程的问题。最后,我们的数值分析说明了随机劳动收入的存在如何影响最优消费和投资策略:A. Bensoussan 受美国国家科学基金会资助[DMS-2204795]。S. Park 由大韩民国教育部和韩国国家研究基金会 [NRF-2022S1A3A2A02089950] 资助。
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Optimal Consumption and Investment with Independent Stochastic Labor Income
We develop a new dynamic continuous-time model of optimal consumption and investment to include independent stochastic labor income. We reduce the problem of solving the Bellman equation to a problem of solving an integral equation. We then explicitly characterize the optimal consumption and investment strategy as a function of income-to-wealth ratio. We provide some analytical comparative statics associated with the value function and optimal strategies. We also develop a quite general numerical algorithm for control iteration and solve the Bellman equation as a sequence of solutions to ordinary differential equations. This numerical algorithm can be readily applied to many other optimal consumption and investment problems especially with extra nondiversifiable Brownian risks, resulting in nonlinear Bellman equations. Finally, our numerical analysis illustrates how the presence of stochastic labor income affects the optimal consumption and investment strategy.Funding: A. Bensoussan was supported by the National Science Foundation under grant [DMS-2204795]. S. Park was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea, South Korea [NRF-2022S1A3A2A02089950].
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来源期刊
Mathematics of Operations Research
Mathematics of Operations Research 管理科学-应用数学
CiteScore
3.40
自引率
5.90%
发文量
178
审稿时长
15.0 months
期刊介绍: Mathematics of Operations Research is an international journal of the Institute for Operations Research and the Management Sciences (INFORMS). The journal invites articles concerned with the mathematical and computational foundations in the areas of continuous, discrete, and stochastic optimization; mathematical programming; dynamic programming; stochastic processes; stochastic models; simulation methodology; control and adaptation; networks; game theory; and decision theory. Also sought are contributions to learning theory and machine learning that have special relevance to decision making, operations research, and management science. The emphasis is on originality, quality, and importance; correctness alone is not sufficient. Significant developments in operations research and management science not having substantial mathematical interest should be directed to other journals such as Management Science or Operations Research.
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