Asymptotic theory of range-based multipower variation

Kim Christensen () and Mark Podolskij ()

CREATES Research Papers from School of Economics and Management, University of Aarhus

Abstract: In this paper, we present a realised 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 returnbased estimators. The analysis is supported by a simulation study and we illustrate the practical use of our framework on some recent TAQ equity data.

Keywords: High-frequency data; Integrated variance; Realised multipower variation; Realised range-basedmultipower variation; Quadratic variation. (search for similar items in EconPapers)
JEL-codes: C10 C80 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-mst
Date: 2011-10-30
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Persistent link: http://EconPapers.repec.org/RePEc:aah:create:2011-47

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