A Tale of Two Time Scales: Determining Integrated Volatility with Noisy High Frequency Data
Per A. Mykland and
No 10111, NBER Working Papers from National Bureau of Economic Research, Inc
It is a common practice in finance to estimate volatility from the sum of frequently-sampled squared returns. However market microstructure poses challenges to this estimation approach, as evidenced by recent empirical studies in finance. This work attempts to lay out theoretical grounds that reconcile continuous-time modeling and discrete-time samples. We propose an estimation approach that takes advantage of the rich sources in tick-by-tick data while preserving the continuous-time assumption on the underlying returns. Under our framework, it becomes clear why and where the usual' volatility estimator fails when the returns are sampled at the highest frequency.
JEL-codes: C32 G12 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-fin
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (17) Track citations by RSS feed
Published as Zhang, Lan, Per A. Mykland and Yacine Ait-Sahalia. "A Tale Of Two Time Scales: Determining Integrated Volatility With Noisy High-Frequency Data," Journal of the American Statistical Association, 2005, v100(472,Dec), 1394-1411.
Downloads: (external link)
Journal Article: A Tale of Two Time Scales: Determining Integrated Volatility With Noisy High-Frequency Data (2005)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:nbr:nberwo:10111
Ordering information: This working paper can be ordered from
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.
Series data maintained by ().