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Empirical likelihood for high frequency data

Camponovo, Lorenzo, Matsushita, Yukitoshi and Otsu, Taisuke (2019) Empirical likelihood for high frequency data. Journal of Business and Economic Statistics. ISSN 0735-0015

[img] Text (Empirical likelihood for high frequency data) - Accepted Version
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Identification Number: 10.1080/07350015.2018.1549051

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: 14 Aug 2019 23:12
URI: http://eprints.lse.ac.uk/id/eprint/100320

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