We analyze the impact of time series dependence in market microstructure
noise on the properties of estimators of the integrated volatility of an
asset price based on data sampled at frequencies high enough for that
noise to be a dominant consideration. We show that combining two time
scales for that purpose will work even when the noise exhibits time series
dependence, analyze in that context a refinement of this approach based on
multiple time scales, and compare empirically our different estimators to
the standard realized volatility.
Keywords: Market microstructure; Serial dependence; High
frequency data; Realized volatility; Subsampling; Two Scales Realized
Volatility; Multiple Scales Realized Volatility