光华讲坛——社会名流与企业家论坛第5479期

主题：High-dimensional Minimum Variance Portfolio Estimation Based on High-frequency Data

主讲人：香港科技大学 李莹莹教授

主持人： 周岭 副教授

时间：2019年7月5日（星期五） 下午15:00-16:00

地点：西南财经大学柳林校区弘远楼408会议室

主办单位：统计研究中心 统计学院 科研处

主讲人简介：

Yingying Li is Professor at the Department of Information System, Business Statistics and Operations Management and the Department of Finance at Hong Kong University of Science and Technology (HKUST). Before joining HKUST, Dr Li also held positions as lecturer and postdoctoral fellow at the Bendheim Center for Finance and the Operations Research and Financial Engineering department at Princeton University.

Dr. Li’s research focuses on high-frequency and/or high-dimensional financial data, volatility estimation and modeling, market microstructure, large portfolio optimization, individualized financial decision making, etc.

She is an elected fellow of the Society for Financial Econometrics (SoFiE). She serves on the editorial boards of Journal of Econometrics, Journal of Business & Economic Statistics and Journal of Financial Econometrics.

Dr. Li received her BSc in Mathematics from Beijing Normal University, and Ph. D in Statistics from the University of Chicago.

李莹莹是香港科技大学（香港科技大学）信息系、商业统计与运营管理系和财务系的教授。 在加入香港科技大学之前，李博士还曾在普林斯顿大学的Bendheim金融中心和运筹学和金融工程系担任讲师和博士后。李博士的研究重点是高频或（和）高维金融数据，波动率估计和建模，市场微观结构，大型投资组合优化，个性化财务决策等。她是金融计量经济学会（SoFiE）的当选研究员。 她是“Journal of Econometrics”，“Journal of Business & Economic Statistics”和“Journal of Financial Econometrics”的编委。李博士在北京师范大学获得数学学士学位，在芝加哥大学获得统计学博士学位。

主要内容：

This paper studies the estimation of high-dimensional minimum variance portfolio (MVP) based on high frequency returns which can exhibit heteroscedasticity \emph{and} possibly be contaminated by microstructure noise. Under certain sparsity assumptions on the precision matrix, we propose an estimator of MVP and prove that our portfolio asymptotically achieves the minimum variance in a sharp sense.  In addition, we introduce consistent estimators of the minimum variance, which provide reference targets.  Simulation and empirical studies demonstrate that our proposed portfolio performs favorably. Based on joint work with Tony Cai, Jianchang Hu and Xinghua Zheng

本文研究了基于高频收益的高维最小方差投资组合（MVP）的估计方法，该方法具有异方差性，且可能受到微观结构噪声的污染。在精度矩阵的某些稀疏性假设下，我们提出了MVP的估计量，并证明了我们的投资组合可以快速趋近最小方差。此外，我们还引入了最小方差的一致估计作为一个参考目标。模拟和实证研究均表明，我们建议的投资组合表现良好。

（本文是与Tony Cai, Jianchang Hu and Xinghua Zheng的共同研究成果）