Empirical likelihood for high frequency data

Lorenzo Camponovo, Yukitoshi Matsushita and Taisuke Otsu

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: This paper introduces empirical likelihood methods for interval estimation and hypothesis testing on volatility measures in some high frequency data environments. We propose a modified empirical likelihood statistic that is asymptotically pivotal under infill asymptotics, where the number of high frequency observations in a fixed time interval increases to infinity. The proposed statistic is extended to be robust to the presence of jumps and microstructure noise. We also provide an empirical likelihood-based test to detect the presence of jumps. Furthermore, we study higher-order properties of a general family of nonparametric likelihood statistics and show that a particular statistic admits a Bartlett correction: a higher-order refinement to achieve better coverage or size properties. Simulation and a real data example illustrate the usefulness of our approach.

Keywords: Nonparametric methods; Volatility; Microstructure noise (search for similar items in EconPapers)
JEL-codes: C1 F3 G3 (search for similar items in EconPapers)
Date: 2019-01-01
New Economics Papers: this item is included in nep-mst and nep-ore
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Published in Journal of Business and Economic Statistics, 1, January, 2019. ISSN: 0735-0015

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:100320

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