英文标题：
《Robust expected utility maximization with medial limits》
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作者：
Daniel Bartl, Patrick Cheridito and Michael Kupper
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最新提交年份：
2018
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英文摘要：
In this paper we study a robust expected utility maximization problem with random endowment in discrete time. We give conditions under which an optimal strategy exists and derive a dual representation for the optimal utility. Our approach is based on a general representation result for monotone convex functionals, a functional version of Choquet\'s capacitability theorem and medial limits. The novelty is that it works under nondominated model uncertainty without any assumptions of time-consistency. As applications, we discuss robust utility maximization problems with moment constraints, Wasserstein constraints and Wasserstein penalties.
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中文摘要：
本文研究了离散时间内具有随机禀赋的鲁棒期望效用最大化问题。我们给出了最优策略存在的条件，并导出了最优效用的对偶表示。我们的方法基于单调凸泛函的一般表示结果、Choquet电容性定理的函数版本和中间极限。新颖之处在于，它在非支配模型不确定性下工作，没有任何时间一致性假设。作为应用，我们讨论了具有矩约束、Wasserstein约束和Wasserstein惩罚的鲁棒效用最大化问题。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Portfolio Management 项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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