  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.
