Nonparametric likelihood for volatility under 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: We propose a nonparametric likelihood inference method for the integrated volatility under high frequency financial data. The nonparametric likelihood statistic, which contains the conventional statistics such as empirical likelihood and Pearson's chi-square as special cases, is not asymptotically pivotal under the so-called infill asymptotics, where the number of high frequency observations in a fixed time interval increases to infinity. We show that multiplying a correction term recovers the chi-square limiting distribution. Furthermore, we establish Bartlett correction for our modified nonparametric likelihood statistic under the constant and general non-constant volatility cases. In contrast to the existing literature, the empirical likelihood statistic is not Bartlett correctable under the infill asymptotics. However, by choosing adequate tuning constants for the power divergence family, we show that the second order refinement to the order n^2 can be achieved.

Keywords: Nonparametric likelihood; Volatility; High frequency data (search for similar items in EconPapers)
JEL-codes: C14 (search for similar items in EconPapers)
Date: 2015-01
New Economics Papers: this item is included in nep-ecm and nep-mst
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https://sticerd.lse.ac.uk/dps/em/em581.pdf (application/pdf)

Related works:
Working Paper: Nonparametric Likelihood for Volatility Under High Frequency Data (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:cep:stiecm:/2015/581

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