2021/06/30 by Carsten Chong, Chong, Carsten H., Thomas Delerue +3 · 2 citations
Economics, Econometrics and Finance · #Complex Systems and Time Series Analysis #FOS: Computer and information sciences #FOS: Economics and business #FOS: Mathematics #Financial Markets and Investment Strategies #Financial Risk and Volatility Modeling #Methodology (stat.ME) #Probability (math.PR) #Statistical Finance (q-fin.ST) #Statistics Theory (math.ST)
paper · pdf · doi:10.48550/arxiv.2106.16149
openalex publication_date 2021/06/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 Itô 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.