High Dimensional Beta Test with High Frequency Data

报告人：冯龙

报告地点：腾讯会议ID:793-996-450

报告时间：2022年10月20日星期四16:00-17:00

报告摘要：

This is the first paper about the high dimensional beta tests with high frequency fifinancial data, which allowing that the number of regressors can be larger than the number of observations within each estimation block and can also grow to infifinity 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 fifinancial data shows the robustness of the proposed Fisher’s combination test under both dense and sparse alternatives.

主讲人简介：

冯龙现任南开大学统计与数据科学学院副教授、特聘研究员、博士生导师。2022年入选南开大学百名青年学科带头人。冯龙于本科毕业于南开大学数学科学学院陈省身数学试点班，博士毕业于南开大学数学科学学院概率论与数理统计专业，获得南开大学优秀博士论文奖。主要从事质量控制、非参数模型、高维数据分析、高频数据分析方面的研究。曾获得2012年教育部学术新人奖，于2012-2014年分别访问香港浸会大学、新加坡国立大学和香港大学，2015年于美国佛罗里达大学做博士后研究。在统计学国际顶尖杂志Journal of American Statistical Association、Biometrika、Annals of Statistics、Journal of Econometrics、Journal of Business and Economic Statistics、Technometrics等发表SCI论文30余篇。曾主持一项国家自然科学基金青年项目，正主持一项国家自然科学基金面上项目，南开大学百青项目一项。