Abstract

http://ssrn.com/abstract=622628
 
 

References (52)



 
 

Citations (67)



 


 



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


Eric Ghysels


University of North Carolina Kenan-Flagler Business School; University of North Carolina (UNC) at Chapel Hill - Department of Economics

Pedro Santa-Clara


New University of Lisbon - Nova School of Business and Economics; National Bureau of Economic Research (NBER); Centre for Economic Policy Research (CEPR)

Rossen I. Valkanov


University of California, San Diego (UCSD) - Rady School of Management

November 2004

NBER Working Paper No. w10914

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.

Number of Pages in PDF File: 45

working papers series


Download This Paper

Date posted: December 8, 2004  

Suggested Citation

Ghysels, Eric and Santa-Clara, Pedro and Valkanov, Rossen I., Predicting Volatility: Getting the Most Out of Return Data Sampled at Different Frequencies (November 2004). NBER Working Paper No. w10914. Available at SSRN: http://ssrn.com/abstract=622628

Contact Information

Eric Ghysels
University of North Carolina Kenan-Flagler Business School ( email )
Kenan-Flagler Business School
Chapel Hill, NC 27599-3490
United States

University of North Carolina (UNC) at Chapel Hill - Department of Economics ( email )
Gardner Hall, CB 3305
Chapel Hill, NC 27599
United States
919-966-5325 (Phone)
919-966-4986 (Fax)
HOME PAGE: http://www.unc.edu/~eghysels/
Pedro Santa-Clara (Contact Author)
New University of Lisbon - Nova School of Business and Economics ( email )
Lisbon
Portugal
HOME PAGE: http://docentes.fe.unl.pt/~psc/
National Bureau of Economic Research (NBER)
1050 Massachusetts Avenue
Cambridge, MA 02138
United States
Centre for Economic Policy Research (CEPR) ( email )
77 Bastwick Street
London, EC1V 3PZ
United Kingdom
Rossen Valkanov
University of California, San Diego (UCSD) - Rady School of Management ( email )
9500 Gilman Drive
Rady School of Management
La Jolla, CA 92093
United States
858-534-0898 (Phone)
Feedback to SSRN


Paper statistics
Abstract Views: 696
Downloads: 51
Download Rank: 21,669
References:  52
Citations:  67

© 2014 Social Science Electronic Publishing, Inc. All Rights Reserved.  FAQ   Terms of Use   Privacy Policy   Copyright   Contact Us
This page was processed by apollo3 in 0.468 seconds