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

TL;DR

This paper introduces econometric tools for estimating integrated volatility from high-frequency data considering dependent microstructure noise, improving accuracy through bias correction and analyzing noise dependence structures.

Contribution

It develops consistent estimators for noise variance and autocovariance, and adapts the pre-averaging method for dependent noise, with a two-step bias correction for better finite sample performance.

Findings

Two-step estimators outperform traditional methods in simulations

Dependence structures of microstructure noise vary across sampling schemes

Accounting for serial dependence improves volatility estimation accuracy

Abstract

In this paper, we develop econometric tools to analyze the integrated volatility 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 a consistent estimator of the integrated volatility, which converges stably to a mixed Gaussian distribution at the optimal rate $n^{1/4}$. To refine the finite sample performance, we propose a two-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 two-step estimators. In an empirical study, we characterize the dependence structures of microstructure noise in several popular sampling schemes 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 integrated volatility.