Working Paper: NBER ID: w11380
Authors: Yacine Atsahalia; Per A. Mykland; Lan Zhang
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 based on multiple time scales, and compare empirically our different estimators to the standard realized volatility.
Keywords: High-frequency data; Volatility estimation; Market microstructure noise
JEL Codes: G12; C22
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Traditional realized volatility (RV) estimator is biased (C51) | High frequencies (C58) |
| High frequencies (C58) | Significant variability in estimates (C13) |
| Two scales realized volatility (TSRV) estimator corrects for bias (C22) | Traditional realized volatility (RV) estimator (C58) |
| Serial dependence in noise (C69) | Asymptotic variance of the RV estimator (C51) |
| Serial dependence in noise (C69) | Bias of the RV estimator (C51) |
| Multiple scales realized volatility (MSRV) estimator achieves faster convergence rates (C58) | Two scales realized volatility (TSRV) estimator (C22) |