Library Header Image
LSE Research Online LSE Library Services

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
Download (533kB)

Identification Number: 10.1080/07350015.2018.1549051


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: 20 Oct 2021 00:56

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics