題目：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.
個人簡介：陳大川，現任南開大學統計與數據科學學院特聘副研究員。研究方向為金融計量，高頻數據分析與高維統計推斷。2019年5月博士畢業于美國伊利諾伊大學芝加哥分校。曾在國際知名雜志Journal of Econometrics, Journal of Business & Economic Statistics和Journal of American Statistical Association上發表多篇論文。2018年獲美國芝加哥大學Stevanovich學生獎學金。2012年獲得南開大學統計學學士學位。2015-2016年在美國芝加哥大學統計學系做訪問學者。
時間：2023年5月11日19:30-20:30
舉辦學院：bat365在线唯一官网登录、統計與數據科學學院