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Journal of Financial Econometrics Advance Access originally published online on December 18, 2006
Journal of Financial Econometrics 2007 5(1):31-67; doi:10.1093/jjfinec/nbl010
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Copyright © The Author 2007. Published by Oxford University Press.

Why Do Absolute Returns Predict Volatility So Well?

Lars Forsberg and Eric Ghysels
     University of Uppsala
     University of North Carolina

eghysels{at}unc.edu


  Abstract

Our objective is volatility forecasting, which is core to many risk management problems. We provide theoretical explanations for (i) the empirical stylized fact recognized at least since Taylor (1986) and Ding, Granger, and Engle (1993) that absolute returns show more persistence than squared returns and (ii) the empirical finding reported in recent work by Ghysels, Santa-Clara, and Valkanov (2006) showing that realized absolute values outperform square return-based volatility measures in predicting future increments in quadratic variation. We start from a continuous time stochastic volatility model for asset returns suggested by Barndorff-Nielsen and Shephard (2001) and study the persistence and linear regression properties of various volatility-related processes either observed directly or with sampling error. We also allow for jumps in the asset return processes and investigate their impact on persistence and linear regression. Extensive empirical results complement the theoretical analysis.

KEYWORDS: MIDAS regressions, realized variance

Received February 28, 2006; revised September 11, 2006; accepted November 8, 2006


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