报告题目：Asset selection based on high frequency Sharpe ratio
报 告 人：陈钊 青年研究员 复旦大学
报告时间：2021年6月25日 下午 14:00-15:00
报告地点：腾讯会议 ID：313 858 058
或点击链接直接加入会议：https://meeting.tencent.com/s/1BNJYhyHfsyQ
校内联系人：赵世舜 zhaoss@jlu.edu.cn
报告摘要：In portfolio choice problem, 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 stationary α-mixing processes. Under α-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.
报告人简介：陈钊，复旦大学大数据学院青年研究员。2012年在中国科学技术大学获得博士学位。之后在美国普林斯顿大学，宾夕法尼亚州立大学从事博士后研究及研究型助理教授工作。科研成果发表在AoS, JASA, JoE，Statistica Sinica, Energy and buildings等期刊上。主要研究方向：高维统计推断，稳健回归，时间序列，非参数及半参数统计方法，以及将统计方法应用于建筑能源，生物信息，癌症研究等领域。
