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

Anderson School of Management Working Paper and UNC Department of Economics Working Paper

46 Pages Posted: 5 Oct 2003  

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

Multiple version iconThere are 2 versions of this paper

Date Written: August 17, 2003

Abstract

We use the MIDAS (Mixed Data Sampling) approach to study regressions of future realized volatility at low-frequency horizons (one to four weeks) on lagged daily and intra-daily (1) squared returns, (2) absolute returns, (3) realized volatility, (4) realized power and (5) return ranges. We document first of all that daily realized power and daily range are surprisingly good predictors of future realized volatility and outperform models based on realized volatility. Moreover, MIDAS models with daily data - range, realized power, realized volatility - require a polynomial with at least 30 days. We document that high-frequency absolute returns are also better at forecasting future low frequency realized volatility than high-frequency squared returns. We also discuss many issues that are encountered in practice, such as long memory and seasonality.
All the results are based on a commonly used FX data set.

Keywords: variance estimation, volatility, asset pricing, MIDAS

JEL Classification: G12, G10, C32, C53

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 (August 17, 2003). Anderson School of Management Working Paper and UNC Department of Economics Working Paper. Available at SSRN: https://ssrn.com/abstract=440941 or http://dx.doi.org/10.2139/ssrn.440941

Eric Ghysels (Contact Author)

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

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)

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