Robust Portfolio Management with Uncertain Compounded Rates of Return

稳健的投资组合管理与不确定的复合回报率

• 批准号: 0757983
• 负责人: Aurelie Thiele
• 金额: $ 20万
• 依托单位: Lehigh University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0757983&HistoricalAwards=false
• 关键词: Robust; Portfolio; Management; Uncertain; Compounded

This grant provides funding to investigate the models, insights and algorithms arising in portfolio management when the uncertain rates of return and the investor's risk preferences are quantified using robust optimization techniques based on uncertainty sets.Robust optimization aims at protecting a system against unknown but bounded disturbances, and hence does not require the precise knowledge of the underlying probability distributions. Furthermore, it allows the expression of risk preferences through the degree of protection chosen by the user. The goal of this research is to explore a variety of analytical approaches that capture the uncertainty drivers observed in real life and the fund manager's attitude towards them, in a framework that is easy to implement by finance practitioners. A novel aspect of the PI's methodology is that she will model the continuously compounded rates of return, rather than the actual returns, as parameters belonging to uncertainty sets, in order to build upon the famous Lognormal model of asset prices while addressing its limitations, and increase the relevance of the robust optimization models for practitioners. If successful, these activities will lead to a comprehensive theory of portfolio management under uncertainty, which will be tightly connected to the information available in practice and the manager's behavior, and will mitigate the uncertainty faced by financial decision-makers while maintaining performance. The PI will analyze models of increasing difficulty, with and without derivatives, with and without short sales, to develop optimal asset allocation guidelines. This research will lead to a better understanding of the following issues: (i) What theoretical insights can be gained regarding the extent of which uncertainty, information and risk preferences affect the optimal strategy? (ii) What are the algorithmic benefits of the robust optimization approach over traditional methods, in particular, in regard to tractability for large-scale problems? The PI's conclusions will be validated through extensive numerical experiments using real data provided by her institution's Financial Services Laboratory.

该基金资助研究投资组合管理中的模型、洞察力和算法，当不确定的回报率和投资者的风险偏好使用基于不确定集的鲁棒优化技术量化时，鲁棒优化旨在保护系统免受未知但有界的干扰，因此不需要精确的基本概率分布知识。此外，它允许通过用户选择的保护程度表达风险偏好。本研究的目标是探索各种分析方法，捕捉在真实的生活中观察到的不确定性驱动因素和基金经理对它们的态度，在一个框架，这是很容易实现的金融从业人员。PI方法的一个新颖之处在于，她将连续复合回报率而不是实际回报率建模为属于不确定性集的参数，以建立在著名的资产价格对数正态模型的基础上，同时解决其局限性，并增加从业者稳健优化模型的相关性。如果成功的话，这些活动将导致一个全面的理论，在不确定性下的投资组合管理，这将是紧密相连的信息在实践中和管理者的行为，并将减轻金融决策者所面临的不确定性，同时保持业绩。PI将分析越来越困难的模型，有和没有衍生品，有和没有卖空，以制定最佳资产配置指南。这项研究将导致更好地理解以下问题：（一）什么样的理论见解，可以获得有关的程度，不确定性，信息和风险偏好影响的最佳策略？(ii)鲁棒优化方法相对于传统方法的算法优势是什么，特别是在大规模问题的易处理性方面？PI的结论将通过使用其机构金融服务实验室提供的真实的数据进行广泛的数值实验来验证。