报告时间：2021年9月22日    下午15:00-16:00

报告地点：腾讯会议（会议ID🫅：980 286 207）

报告嘉宾🤘🏻：冯龙

报告主题：High-dimensional test for alpha in linear factor pricing models with sparse alternatives

报告摘要☆：We consider the problem of testing for the presence of alpha in Linear Factor Pricing Models. We propose a novel test of the max-of-squares type, which is designed to deal with the high dimensionality of the securities and the sparse alternatives. We rigorously show that the proposed test has attractive theoretical properties and demonstrate its superior performance via Monte Carlo experiments. These results are established when the number of securities is larger than the time dimension of the return series, and the alternative hypothesis is sparse, i.e. the alpha vector is sparse. As a general alternative, we suggest to combine the max-of-squares type test and a sum-of-squares type test, to benefit from the power advantages of both tests. We apply the two proposed tests to the monthly returns on securities in the Chinese and the U.S. stock markets, respectively under the Fama–French three-factor model, and confirm the usefulness of the proposed tests in detecting the presence of alpha.

报告人简介：冯龙，副教授。本科毕业于南开大学数学试点班🏹👨🏿，博士毕业于南开大学。研究方向包括高维数据分析🙆‍♂️、计量经济学等。在国际高水平杂志JASA, AOS, Biometrika, JOE, JBES, Technometrics等发表SCI论文20余篇。曾主持一项国家自然科学基金青年项目🙎🏽。