# 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.

## Key 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.

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Source: https://tomesphere.com/paper/1704.08964