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arXiv 2211.00532q-fin.MF
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具有比例交易成本的càdlàg价格过程下的稳健效用最大化

Robust utility maximisation under proportional transaction costs for càdlàg price processes

Christoph Czichowsky, Raphael Huwyler

AI总结:

本文在Chau和Rásonyi框架下研究比例交易成本与模型不确定性下的稳健效用最大化,将最优交易策略存在性结果推广至一般严格正càdlàg价格过程和不完备滤子情形,并肯定回答了相关开放问题。

摘要:

我们考虑在模型不确定性下,具有比例交易成本的连续时间金融市场中的稳健效用最大化。为此,我们在Chau和Rásonyi(2019)的框架下工作,该框架通过在同一基础滤子概率空间上给定的可能不可数模型类上最大化最坏情况预期效用来实现稳健性。在此设置下,我们给出了最优交易策略存在的充分条件,将Chau和Rásonyi(2019)关于正半轴上效用函数的结果从连续价格过程推广到一般严格正càdlàg价格过程,并从完备滤子推广到不完备滤子。我们的结果对Chau和Rásonyi(2019)中指出的一个开放问题给出了肯定回答,并表明嵌入到可数乘积空间并非必要。

英文摘要:

We consider robust utility maximisation in continuous-time financial markets with proportional transaction costs under model uncertainty. For this purpose, we work in the framework of Chau and Rásonyi (2019), where robustness is achieved by maximising the worst-case expected utility over a possibly uncountable class of models that are all given on the same underlying filtered probability space. In this setting, we give sufficient conditions for the existence of an optimal trading strategy, extending the result for utility functions on the positive half-line of Chau and Rásonyi (2019) from continuous to general strictly positive càdlàg price processes and from complete to incomplete filtrations. Our result allows us to provide a positive answer to an open question pointed out in Chau and Rásonyi (2019), and shows that the embedding into a countable product space is not essential.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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