题 目:Asset selection based on high frequency Sharpe ratio

报告人:陈敏，中国科学院数学与系统科学研究院研究员,博士生导师

报告平台：腾讯会议，ID：610-299-493

时 间：2021年12月6号下午4:00-5:30

邀请人：刘三阳

报告人简介：中国科学院数学与系统科学研究院研究员,博士生导师，享受国务院政府特殊津贴。现任全国统计方法应用技术标准化委员会主任委员,《数理统计与管理》主编,《应用数学学报（中文版）》副主编，全国工业统计学教学研究会会长。曾任中国数学学会副理事长、中国统计教育学会副会长、北京大数据协会副会长。曾任中国科学院数学与系统科学研究院副院长。

主要研究方向为：金融统计理论与方法、非线性时间序列的统计分析，非参数统计估计和检验的大样本理论，生物统计的理论和方法，应用统计（工业统计、统计标准化、财税信息技术），大数据分析与处理的统计理论与算法研究。出版翻译教材和专著7部；在国内外核心学术期刊发表统计理论与应用、经济、金融和管理科学论文150余篇，其中SCI 80余篇。

Abstract: In portfolio choice problems, the classical Mean–Variance model in Markowitz (1952) relies heavily on the covariance structure among assets. As the number and types of assets increase rapidly, traditional methods to estimate the covariance matrix and its inverse suffer from the common issues in high or ultra-high dimensional analysis. To avoid the issue of estimating the covariance matrix with high or ultra-high dimensional data, we propose a fast procedure to reduce dimension based on a new risk/return measure constructed from intra-day high frequency data and select assets via Dependent Sure Explained Variability and Independence Screening (D-SEVIS). While most feature screening methods assume i.i.d. samples, by nature of our data, we make contribution to studying D-SEVIS for samples with serial correlation, specifically, for the stationaryα-mixing processes. Underα-mixing condition, we prove that D-SEVIS satisfies sure screening property and ranking consistency property. More importantly, with the assets selected through D-SEVIS, we will build a portfolio that earns more excess return compared with several existing portfolio allocation methods. We illustrate this advantage of our asset selection method with the real data from the stock market.