Empirical likelihood for high frequency data

Camponovo, Lorenzo, Matsushita, Yukitoshi and Otsu, Taisuke ORCID: 0000-0002-2307-143X (2019) Empirical likelihood for high frequency data. Journal of Business and Economic Statistics. ISSN 0735-0015

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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.

Item Type:	Article
Additional Information:	© 2019 American Statistical Association
Divisions:	Economics
Subjects:	H Social Sciences > HG Finance H Social Sciences > HA Statistics
Date Deposited:	26 Mar 2019 12:33
Last Modified:	01 Nov 2024 05:31
URI:	http://eprints.lse.ac.uk/id/eprint/100320

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