Asymptotic theory of range-based multipower variation
AbstractIn 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 InfoPaper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2011-47.
Date of creation: 30 Oct 2011
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Web page: http://www.econ.au.dk/afn/
High-frequency data; Integrated variance; Realised multipower variation; Realised range-basedmultipower variation; Quadratic variation.;
Other versions of this item:
- Kim Christensen & Mark Podolskij, 2012. "Asymptotic Theory of Range-Based Multipower Variation," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 10(3), pages 417-456, June.
- C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
- C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-12-19 (All new papers)
- NEP-ECM-2011-12-19 (Econometrics)
- NEP-ETS-2011-12-19 (Econometric Time Series)
- NEP-MST-2011-12-19 (Market Microstructure)
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- Massimiliano Caporin & Eduardo Rossi & Paolo Santucci de Magistris, 2014. "Volatility jumps and their economic determinants," CREATES Research Papers 2014-27, School of Economics and Management, University of Aarhus.
- Vortelinos, Dimitrios I., 2014. "Optimally sampled realized range-based volatility estimators," Research in International Business and Finance, Elsevier, Elsevier, vol. 30(C), pages 34-50.
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