随机系数制度交换市场中有约束的最优消费-投资

IF 1.6 2区 数学 Q2 MATHEMATICS, APPLIED
Ying Hu, Xiaomin Shi, Zuo Quan Xu
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引用次数: 0

摘要

本文研究了具有随机系数且可能受非凸约束的状态切换市场中具有功率、对数和指数效用的有限时间最优消费-投资问题。与现有模型相比,我们的模型的一个显著特征是,在电力和对数公用事业的情况下,对消费和投资策略的交易约束是耦合在一起的,这导致了新的技术挑战。我们给出了这些消费-投资问题的显式最优消费-投资策略和最优值,这些最优值用一些多维对角二次倒向随机微分方程(BSDE)和无界系数线性BSDE的解来表示。这些bsde在文献中是新的,求解它们是本文的主要理论贡献之一。我们采用截断、近似的方法,得到了它们解的先验一致的下界和上界。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal Consumption–Investment with Constraints in a Regime Switching Market with Random Coefficients

This paper studies finite-time optimal consumption–investment problems with power, logarithmic and exponential utilities, in a regime switching market with random coefficients and possibly subject to non-convex constraints. Compared to the existing models, one distinguish feature of our model is that the trading constraints put on the consumption and investment strategies are coupled together in the cases of power and logarithmic utilities, leading to new technical challenges. We provide explicit optimal consumption–investment strategies and optimal values for these consumption–investment problems, which are expressed in terms of the solutions to some multi-dimensional diagonally quadratic backward stochastic differential equation (BSDE) and linear BSDE with unbound coefficients. These BSDEs are new in the literature and solving them is one of the main theoretical contributions of this paper. We accomplish the latter by applying the truncation, approximation technique to get some a priori uniformly lower and upper bounds for their solutions.

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来源期刊
CiteScore
3.30
自引率
5.60%
发文量
103
审稿时长
>12 weeks
期刊介绍: The Applied Mathematics and Optimization Journal covers a broad range of mathematical methods in particular those that bridge with optimization and have some connection with applications. Core topics include calculus of variations, partial differential equations, stochastic control, optimization of deterministic or stochastic systems in discrete or continuous time, homogenization, control theory, mean field games, dynamic games and optimal transport. Algorithmic, data analytic, machine learning and numerical methods which support the modeling and analysis of optimization problems are encouraged. Of great interest are papers which show some novel idea in either the theory or model which include some connection with potential applications in science and engineering.
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