Ultra high frequency volatility estimation with dependent microstructure noise |
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| Authors: | Yacine Aï t-Sahalia,Per A. Mykland,Lan Zhang |
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| Affiliation: | 1. Princeton University and NBER, Department of Economics, Princeton, NJ 08544-1021, United States;2. The University of Chicago, United States;3. University of Illinois - Chicago, United States |
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| Abstract: | 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 is based on multiple time scales, and compare empirically our different estimators to the standard realized volatility. |
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| Keywords: | Market microstructure Serial dependence High frequency data Realized volatility Subsampling Two scales realized volatility Multiple scales realized volatility |
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