题目：High Dimensional Beta Test with High Frequency Data
摘要：This is the first paper about the high dimensional beta tests with high frequency financial data, which allow the number of regressors be larger than the number of observations within each estimation block and can grow to infinity in asymptotics. In this paper, the sum-type test and max-type test have been proposed, where the sum-type test is suitable for the dense alternative and the max-type test is suitable for the sparse alternative. By showing the asymptotic independence between the sum-type test and max-type test, a Fisher's combination test is proposed, which is robust to both dense and sparse alternatives. The limiting null distributions of the three proposed tests are derived and the asymptotic behavior of their powers are also analyzed. Monte Carlo simulations demonstrate the validity of the theoretical results developed in this paper. Empirical study with real high frequency financial data shows the robustness of the proposed Fisher's combination test under both dense and sparse alternatives. This is the joint work with Long Feng, Per Mykland and Lan Zhang.
个人简介：陈大川，现任南开大学统计与数据科学学院特聘副研究员。研究方向为金融计量，高频数据分析与高维统计推断。2019年5月博士毕业于美国伊利诺伊大学芝加哥分校。曾在国际知名杂志Journal of Econometrics, Journal of Business & Economic Statistics和Journal of American Statistical Association上发表多篇论文。2018年获美国芝加哥大学Stevanovich学生奖学金。2012年获得南开大学统计学学士学位。2015-2016年在美国芝加哥大学统计学系做访问学者。
时间：2023年5月11日19:30-20:30
举办学院：数学学院、统计与数据科学学院