When Frictions Are Fractional: Rough Noise in High-Frequency Data

• 成果类型: Article; Early Access
• 署名作者: Chong, Carsten H.; Delerue, Thomas; Li, Guoying
• 署名单位: Hong Kong University of Science & Technology; Columbia University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2024.2428466
• 发表日期: 2024
• 关键词: MICROSTRUCTURE NOISE; INTEGRATED VOLATILITY; generalized-method; ASYMPTOTIC THEORY; DYNAMICS; moments

摘要

The analysis of high-frequency financial data is often impeded by the presence of noise. This article is motivated by intraday return data in which market microstructure noise appears to be rough, that is, best captured by a continuous-time stochastic process that locally behaves as fractional Brownian motion. Assuming that the underlying efficient price process follows a continuous It & ocirc; semimartingale, we derive consistent estimators and asymptotic confidence intervals for the roughness parameter of the noise and the integrated price and noise volatilities, in all cases where these quantities are identifiable. In addition to desirable features such as serial dependence of increments, compatibility between different sampling frequencies and diurnal effects, the rough noise model can further explain divergence rates in volatility signature plots that vary considerably over time and between assets. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.