Asymptotic Theory of Range-Based Multipower Variation.

In this paper, we present a realized range-based multipower variation theory, which can be used to estimate return variation and draw jump-robust inference about the diffusive volatility component, when a high-frequency record of asset prices is available. The standard range-statistic—routinely used in financial economics to estimate the variance of securities prices—is shown to be biased when the price process contains jumps. We outline how the new theory can be applied to remove this bias by constructing a hybrid range-based estimator. Our asymptotic theory also reveals that when high-frequency data are sparsely sampled, as is often done in practice due to the presence of microstructure noise, the range-based multipower variations can produce significant efficiency gains over comparable subsampled return-based estimators. The analysis is supported by a simulation study, and we illustrate the practical use of our framework on some recent Trade and Quote (TAQ) equity data. [ABSTRACT FROM PUBLISHER]