Ultra High Frequency Volatility Estimation with Dependent Microstructure Noise

Yacine Ait-Sahalia, Per A. Mykland and Lan Zhang

No 11380, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: We analyze the impact of time series dependence in market microstructure noise on the properties of estimators of the integrated volatility of an asset price based on data sampled at frequencies high enough for that noise to be a dominant consideration. We show that combining two time scales for that purpose will work even when the noise exhibits time series dependence, analyze in that context a refinement of this approach based on multiple time scales, and compare empirically our different estimators to the standard realized volatility.

JEL-codes: C22 G12 (search for similar items in EconPapers)
Date: 2005-05
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-fin
Note: AP
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (34)

Published as Aït-Sahalia, Yacine & Mykland, Per A. & Zhang, Lan, 2011. "Ultra high frequency volatility estimation with dependent microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 160-175, January.

Downloads: (external link)
http://www.nber.org/papers/w11380.pdf (application/pdf)

Related works:
Journal Article: Ultra high frequency volatility estimation with dependent microstructure noise (2011)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nbr:nberwo:11380

Ordering information: This working paper can be ordered from
http://www.nber.org/papers/w11380

Access Statistics for this paper

More papers in NBER Working Papers from National Bureau of Economic Research, Inc National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.. Contact information at EDIRC.
Bibliographic data for series maintained by (wpc@nber.org).