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

Z. Merrick Li, Roger Laeven and Michel H. Vellekoop

Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge

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 (search for similar items in EconPapers)
JEL-codes: C13 C14 C55 C58 (search for similar items in EconPapers)
Date: 2019-06-14
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-mst, nep-ore and nep-rmg
Note: ml882
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Journal Article: Dependent microstructure noise and integrated volatility estimation from high-frequency data (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:cam:camdae:1952

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