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Asymptotic theory of range-based multipower variation

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Author Info

  • Kim Christensen

    ()
    (Aarhus University and CREATES)

  • Mark Podolskij

    ()
    (University of Heidelberg and CREATES)

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.

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Bibliographic Info

Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2011-47.

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Length: 53
Date of creation: 30 Oct 2011
Date of revision:
Handle: RePEc:aah:create:2011-47

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Web page: http://www.econ.au.dk/afn/

Related research

Keywords: High-frequency data; Integrated variance; Realised multipower variation; Realised range-basedmultipower variation; Quadratic variation.;

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Cited by:
  1. Vortelinos, Dimitrios I., 2014. "Optimally sampled realized range-based volatility estimators," Research in International Business and Finance, Elsevier, vol. 30(C), pages 34-50.

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