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Empirical likelihood for high frequency data

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  • Lorenzo Camponovo
  • Yukitoshi Matsushita
  • Taisuke Otsu

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.

Suggested Citation

  • Lorenzo Camponovo & Yukitoshi Matsushita & Taisuke Otsu, 2020. "Empirical likelihood for high frequency data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(3), pages 621-632, July.
    • Handle: RePEc:taf:jnlbes:v:38:y:2020:i:3:p:621-632
    DOI: 10.1080/07350015.2018.1549051
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    Cited by:

    1. Gong, Xiao-Li & Liu, Jian-Min & Xiong, Xiong & Zhang, Wei, 2022. "Research on stock volatility risk and investor sentiment contagion from the perspective of multi-layer dynamic network," International Review of Financial Analysis, Elsevier, vol. 84(C).

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