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In this paper, we develop econometric tools to analyze the integratedvolatility of the efficient price and the dynamic properties of microstructurenoise in high-frequency data under general dependent noise. We first developconsistent estimators of the variance and autocovariances of noise using avariant of realized volatility. Next, we employ these estimators to adapt thepre-averaging method and derive a consistent estimator of the integratedvolatility, which converges stably to a mixed Gaussian distribution at theoptimal rate $n^{1/4}$. To refine the finite sample performance, we propose atwo-step approach that corrects the finite sample bias, which turns out to becrucial in applications. Our extensive simulation studies demonstrate theexcellent performance of our two-step estimators. In an empirical study, wecharacterize the dependence structures of microstructure noise in severalpopular sampling schemes and provide intuitive economic interpretations; wealso illustrate the importance of accounting for both the serial dependence innoise and the finite sample bias when estimating integrated volatility.

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