Empirical likelihood for high frequency data

Lorenzo Camponovo, Yukitoshi Matsushita, Taisuke Otsu

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)621-632
Number of pages12
JournalJournal of Business and Economic Statistics
Issue number3
Publication statusPublished - 2020 Jul 2
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty


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