文章基本信息

标题：Nonparametric likelihood for volatility under 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：2015
出版社：Suntory Toyota International Centre for Economics and Related Disciplines
摘要：We propose a nonparametric likelihood inference method for the integrated volatility under high frequency financial data. The nonparametric likelihood statistic, which contains the conventional statistics such as empirical likelihood and Pearson’s chi-square as special cases, is not asymptotically pivotal under the so-called infill asymptotics, where the number of high frequency observations in a fixed time interval increases to infinity. We show that multiplying a correction term recovers the chi-square limiting distribution. Furthermore, we establish Bartlett correction for our modified nonparametric likelihood statistic under the constant and general non-constant volatility cases. In contrast to the existing literature, the empirical likelihood statistic is not Bartlett correctable under the infill asymptotics. However, by choosing adequate tuning constants for the power divergence family, we show that the second order refinement to the order n^2 can be achieved.
关键词：nonparametric likelihood ; volatility ; high frequency data