Empirical likelihood for high frequency data

Lorenzo Camponovo, Yukitoshi Matsushita and Taisuke Otsu

STICERD - Econometrics Paper Series from Suntory and Toyota International Centres for Economics and Related Disciplines, LSE

Abstract: With increasing availability of high frequency financial data as a background, various volatility measures and related statistical theory are developed in the recent literature. This paper introduces the method of empirical likelihood to conduct statistical inference on the volatility measures under high frequency data environments. We propose a modified empirical likelihood statistic that is asymptotically pivotal under the infill asymptotics, where the number of high frequency observations in a fixed time interval increases to infinity. Our empirical likelihood approach is extended to be robust to the presence of jumps and microstructure noise. We also provide an empirical likelihood test to detect presence of jumps. Furthermore, we establish Bartlett correction, a higher-order refinement, for a general nonparametric likelihood statistic. Simulation and a real data example illustrate the usefulness of our approach.

Keywords: High frequency data; Volatility; Empirical likelihood (search for similar items in EconPapers)
JEL-codes: C12 C14 C58 (search for similar items in EconPapers)
Date: 2017-02
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-mst and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:cep:stiecm:591

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