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Faculty of Economics

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Li, Z. M., Laeven, R. J. A. and Vellekoop, M. H.

Dependent Microstructure Noise and Integrated Volatility: Estimation from High-Frequency Data

Journal of Econometrics, accepted

(2019)

Abstract: In this paper, we develop econometric tools to analyze the integrated volatility (IV) of the efficient price and the dynamic properties of microstructure noise in high-frequency data under general dependent noise. We first develop consistent estimators of the variance and autocovariances of noise using a variant of realized volatility. Next, we employ these estimators to adapt the pre-averaging method and derive consistent estimators of the IV, which converge stably to a mixed Gaussian distribution at the optimal rate n1/4. To improve the finite sample performance, we propose a multi-step approach that corrects the finite sample bias, which turns out to be crucial in applications. Our extensive simulation studies demonstrate the excellent performance of our multi-step estimators. In an empirical study, we analyze the dependence structures of microstructure noise and provide intuitive economic interpretations; we also illustrate the importance of accounting for both the serial dependence in noise and the finite sample bias when estimating IV.

Keywords: Dependent microstructure noise, realized volatility, bias correction, integrated volatility, mixing sequences, pre-averaging method

JEL Codes: C13, C14, C55, C58

Author links: Merrick Li  

Publisher's Link: https://doi.org/10.1016/j.jeconom.2019.10.004



Cambridge Working Paper in Economics Version of Paper: Dependent Microstructure Noise and Integrated Volatility: Estimation from High-Frequency Data, Li, Z. M., Laeven, R. J. A. and Vellekoop, M. H., (2019)

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Li, Z. M., Laeven, R. J. A. and Vellekoop, M. H. Dependent Microstructure Noise and Integrated Volatility: Estimation from High-Frequency Data Journal of Econometrics, accepted [2019]