Empirical likelihood for high frequency data

Lorenzo Camponovo, Yukitoshi Matsushita, Taisuke Otsu

研究成果: Article

抄録

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.

元の言語English
ジャーナルJournal of Business and Economic Statistics
DOI
出版物ステータスPublished - 2019 1 1
外部発表Yes

Fingerprint

High-frequency Data
Empirical Likelihood
Statistic
statistics
Infill Asymptotics
Jump
Nonparametric Likelihood
Bartlett Correction
Modified Likelihood
Higher Order
Interval Estimation
Likelihood Methods
Hypothesis Testing
Volatility
empirical method
hypothesis testing
Microstructure
Coverage
Refinement
Infinity

ASJC Scopus subject areas

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

これを引用

Empirical likelihood for high frequency data. / Camponovo, Lorenzo; Matsushita, Yukitoshi; Otsu, Taisuke.

:: Journal of Business and Economic Statistics, 01.01.2019.

研究成果: Article

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