Jump-Robust Volatility Estimation using Nearest Neighbor Truncation
Torben Andersen (),
Dobrislav Dobrev () and
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Dobrislav Dobrev: Federal Reserve Board of Governors, Postal: Federal Reserve Board of Governors
CREATES Research Papers from Department of Economics and Business Economics, Aarhus University
We propose two new jump-robust estimators of integrated variance based on highfrequency return observations. These MinRV and MedRV estimators provide an attractive alternative to the prevailing bipower and multipower variation measures. Specifically, the MedRV estimator has better theoretical efficiency properties than the tripower variation measure and displays better finite-sample robustness to both jumps and the occurrence of “zero” returns in the sample. Unlike the bipower variation measure, the new estimators allow for the development of an asymptotic limit theory in the presence of jumps. Finally, they retain the local nature associated with the low order multipower variation measures. This proves essential for alleviating finite sample biases arising from the pronounced intraday volatility pattern which afflict alternative jump-robust estimators based on longer blocks of returns. An empirical investigation of the Dow Jones 30 stocks and an extensive simulation study corroborate the robustness and efficiency properties of the new estimators.
Keywords: High-frequency data; Integrated variance; Finite activity jumps; Realized volatility; Jump robustness; Nearest neighbor truncation (search for similar items in EconPapers)
JEL-codes: C14 C15 C22 C80 G10 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-mst
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Journal Article: Jump-robust volatility estimation using nearest neighbor truncation (2012)
Working Paper: Jump-robust volatility estimation using nearest neighbor truncation (2010)
Working Paper: Jump-Robust Volatility Estimation using Nearest Neighbor Truncation (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:aah:create:2009-52
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