Skip Navigation


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
This Article
Right arrow Full Text
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
5/1/31    most recent
nbl010v3
nbl010v2
nbl010v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Forsberg, L.
Right arrow Articles by Ghysels, E.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

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


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 JOURNAL OF FINANCIAL ECONOMETRICSHome page
C. Liu and J. M. Maheu
Are There Structural Breaks in Realized Volatility?
J. Financial Econometrics, July 1, 2008; 6(3): 326 - 360.
[Abstract] [Full Text] [PDF]


Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.