Robust utility maximization with extremely ambiguity-loving and ambiguity-aversion preferences

被引:5
|
作者
Li, Bin [1 ]
Wang, Lihe [2 ,3 ]
Xiong, Dewen [3 ]
机构
[1] Univ Waterloo, Dept Stat & Actuarial Sci, Waterloo, ON, Canada
[2] Univ Iowa, Dept Math, Iowa City, IA 52242 USA
[3] Shanghai Jiao Tong Univ, Dept Math, Shanghai, Peoples R China
基金
上海市自然科学基金; 中国国家自然科学基金; 加拿大自然科学与工程研究理事会;
关键词
Ambiguity; backward stochastic differential equations; martingale characterization; robust utility maximization; nonlinear expectations; MODEL UNCERTAINTY; RISK;
D O I
10.1080/17442508.2017.1371176
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
In this paper, we investigate the robust utilitymaximization problems under both preferences of extremely ambiguity loving and ambiguity aversion. By a fundamental martingale characterization technique on nonlinear expectations, optimal investment strategies are explicitly solved in a general non- Markovian framework via backward stochastic differential equations. Different with previous works in the literature assuming the convexity of the set of prior probabilitymeasuresQ, our analysis are independent of the cardinality ofQ. Our results show that extremely ambiguity- loving ( resp. - aversion) investors will adopt the extremely aggressive ( resp. conservative) investment strategy.
引用
收藏
页码:524 / 538
页数:15
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