Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies

Eric Ghysels (), Pedro Santa-Clara and Rossen Valkanov

No 10914, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: We consider various MIDAS (Mixed Data Sampling) regression models to predict volatility. The models differ in the specification of regressors (squared returns, absolute returns, realized volatility, realized power, and return ranges), in the use of daily or intra-daily (5-minute) data, and in the length of the past history included in the forecasts. The MIDAS framework allows us to compare models across all these dimensions in a very tightly parameterized fashion. Using equity return data, we find that daily realized power (involving 5-minute absolute returns) is the best predictor of future volatility (measured by increments in quadratic variation) and outperforms model based on realized volatility (i.e. past increments in quadratic variation). Surprisingly, the direct use of high-frequency (5-minute) data does not improve volatility predictions. Finally, daily lags of one to two months are sucient to capture the persistence in volatility. These findings hold both in- and out-of-sample.

JEL-codes: G1 (search for similar items in EconPapers)
Date: 2004-11
New Economics Papers: this item is included in nep-fin
Note: AP
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

Published as Ghysels, Eric, Pedro Santa-Clara and Rossen Valkanov. "Predicting Volatility: Getting The Most Our Of Return Data Sampled At Different Frequencies," Journal of Econometrics, 2006, v131(1-2,Mar-Apr), 59-95.

Downloads: (external link)
http://www.nber.org/papers/w10914.pdf (application/pdf)

Related works:
Journal Article: Predicting volatility: getting the most out of return data sampled at different frequencies (2006)
Working Paper: Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies (2004)
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:10914

Ordering information: This working paper can be ordered from
http://www.nber.org/papers/w10914

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 ().