文章基本信息

标题：Empirical likelihood for high frequency data
本地全文：下载
作者：Lorenzo Camponovo ; Yukitoshi Matsushita ; Taisuke Otsu 等
期刊名称：Econometrics Publications
印刷版ISSN：0969-4366
出版年度：2017
出版社：Suntory Toyota International Centre for Economics and Related Disciplines
摘要：With increasing availability of high frequency financial data as a background, various volatility measures and related statistical theory are developed in the recent literature. This paper introduces the method of empirical likelihood to conduct statistical inference on the volatility measures under high frequency data environments. We propose a modified empirical likelihood statistic that is asymptotically pivotal under the infill asymptotics, where the number of high frequency observations in a fixed time interval increases to infinity. Our empirical likelihood approach is extended to be robust to the presence of jumps and microstructure noise. We also provide an empirical likelihood test to detect presence of jumps. Furthermore, we establish Bartlett correction, a higher-order refinement, for a general nonparametric likelihood statistic. Simulation and a real data example illustrate the usefulness of our approach.
关键词：high frequency data ; volatility ; empirical likelihood