管理科学与工程系学术讲座

题目: 环境信息下基于高斯混合模型的鲁棒投资组合优化

 Robust Contextual Portfolio Optimization with Gaussian Mixture Models

演讲人: 王逸杰，德克萨斯大学奥斯汀分校

时间: 2022年11月3日上午 10:00

地点: 腾讯会议

会议号：:618 342 194

会议密码：483288

讲座摘要:

We consider the portfolio optimization problem with contextual information that is available to better quantify and predict the uncertain returns of assets. Motivated by the regime modeling techniques for the finance market, we consider the setting where both the uncertain returns and the contextual information follow a Gaussian Mixture (GM) distribution. This problem is shown to be equivalent to a nominal portfolio optimization problem where the means and the covariance matrix are adjusted by the contextual information. We then apply robust optimization and propose the robust contextual portfolio optimization problem, which reduces the sensitivity of model parameters used in the Gaussian Mixture Model (GMM). A tractable formulation is derived to approximate the solution of the robust contextual portfolio optimization problem. We conduct a numerical experiment in the US equity markets, and the results demonstrate the advantage of our proposed model against other benchmark methods.

演讲嘉宾简介:

王逸杰，德克萨斯大学奥斯汀分校运筹学博士生，导师为Grani Hanasusanto。2018年获得北京航天航空大学自动化专业学士学位及数学双学士学位。目前研究方向聚焦于随机优化，鲁棒优化，分布式鲁棒优化，以及它们在机器学习、排队论、金融量化等领域的应用。