STATISTICAL PROPERTIES OF MICROSTRUCTURE NOISE

• 成果类型: Article
• 署名作者: Jacod, Jean; Li, Yingying; Zheng, Xinghua
• 署名单位: Universite Paris Cite; Sorbonne Universite; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI); Sorbonne Universite; Hong Kong University of Science & Technology; Hong Kong University of Science & Technology
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA13085
• 发表日期: 2017
• 页码: 1133-1174
• 关键词: High-frequency data; Bid-ask spread; INTEGRATED VOLATILITY; efficient estimation; realized variance; MARKET

摘要

We study the estimation of (joint) moments of microstructure noise based on high frequency data. The estimation is conducted under a nonparametric setting, which allows the underlying price process to have jumps, the observation times to be irregularly spaced, and the noise to be dependent on the price process and to have diurnal features. Estimators of arbitrary orders of (joint) moments are provided, for which we establish consistency as well as central limit theorems. In particular, we provide estimators of autocovariances and autocorrelations of the noise. Simulation studies demonstrate excellent performance of our estimators in the presence of jumps, irregular observation times, and even rounding. Empirical studies reveal (moderate) positive autocorrelations of microstructure noise for the stocks tested.