報告題目：Asset selection based on high frequency Sharpe ratio
報 告 人：陳钊 青年研究員 複旦大學
報告時間：2021年6月25日 下午 14:00-15:00
報告地點：騰訊會議 ID：313 858 058
或點擊鍊接直接加入會議：https://meeting.tencent.com/s/1BNJYhyHfsyQ
校内聯系人：趙世舜 zhaoss@jlu.edu.cn
報告摘要：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.
報告人簡介：陳钊，複旦大學大數據學院青年研究員。2012年在中國科學技術大學獲得博士學位。之後在美國普林斯頓大學，賓夕法尼亞州立大學從事博士後研究及研究型助理教授工作。科研成果發表在AoS, JASA, JoE，Statistica Sinica, Energy and buildings等期刊上。主要研究方向：高維統計推斷，穩健回歸，時間序列，非參數及半參數統計方法，以及将統計方法應用于建築能源，生物信息，癌症研究等領域。
