Mean-Variance optimization in discrete-time decision processes with general utility function☆

被引：0
作者：
Baeuerle, Nicole [1 ]
Jaskiewicz, Anna [2 ]
Nowak, Andrzej S. [3 ]
机构：
[1] Karlsruhe Inst Technol, Dept Math, Karlsruhe, Germany
[2] Wroclaw Univ Sci & Technol, Fac Pure & Appl Math, Wroclaw, Poland
[3] Univ Zielona Gora, Inst Math, Zielona Gora, Poland
来源：
AUTOMATICA | 2025年 / 174卷
关键词：
Mean-variance optimization; Stochastic dynamic programming; Decision process; Portfolio selection; Investment decisions; similar technologies; OPTIMAL POLICIES; COMPACTNESS; TRADEOFFS;
D O I：
10.1016/j.automatica.2025.112142
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We study general discrete-time Mean-Variance problems in a non-Markovian setting. The utility is a general, continuous function which may depend on the entire history of the process. It contains many recursive utility functions with non-linear aggregator as special cases. Under some continuity and compactness assumptions on the model data, we establish the existence of persistently optimal deterministic policies. For finite horizon problems this also yields a recursive solution algorithm. The theory which we develop here goes beyond Mean-Variance models and may be applied, e.g., to Optimized Certainty Equivalents. The Mean-Variance optimization framework is also applied to a multi-stage portfolio analysis with constraints on short selling. (c) 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and
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页数：9
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