1. Dependent microstructure noise and integrated volatility estimation from high-frequency data
- Author
-
Michel Vellekoop, Z. Merrick Li, Roger J. A. Laeven, Faculteit Economie en Bedrijfskunde, and Actuarial Science & Mathematical Finance (ASE, FEB)
- Subjects
Mathematics, Interdisciplinary Applications ,Economics and Econometrics ,Pre-averaging method ,Realized variance ,Computer science ,Economics ,Gaussian ,Frequency data ,Social Sciences ,Mathematics - Statistics Theory ,Statistics Theory (math.ST) ,EFFICIENT ESTIMATION ,symbols.namesake ,Dependent microstructure noise ,Empirical research ,Integrated volatility ,PRICES ,JUMPS ,Business & Economics ,BID-ASK SPREAD ,FOS: Mathematics ,Applied mathematics ,REALIZED VARIANCE ,Sampling bias ,Science & Technology ,Applied Mathematics ,COMPONENTS ,Estimator ,Social Sciences, Mathematical Methods ,Microstructure ,TIME ,MARKET ,Physical Sciences ,symbols ,Bias correction ,Volatility (finance) ,Realized volatility ,Mathematics ,Mathematical Methods In Social Sciences - 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.
- Published
- 2020