Skip Navigation


Journal of Financial Econometrics Advance Access originally published online on August 26, 2005
Journal of Financial Econometrics 2005 3(4):525-554; doi:10.1093/jjfinec/nbi028
This Article
Right arrow Full Text
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
3/4/525    most recent
nbi028v1
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 Hansen, P. R.
Right arrow Articles by Lunde, A.
Right arrow Search for Related Content
Related Collections
Right arrow C22 - Time-Series Models
Right arrow G12 - Asset Pricing; Trading volume; Bond Interest Rates
Right arrow G14 - Information and Market Efficiency; Event Studies
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2005. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oupjournals.org.

A Realized Variance for the Whole Day Based on Intermittent High-Frequency Data

Peter Reinhard Hansen
     Stanford University

Asger Lunde
     Aarhus School of Business

Address correspondence to Asger Lunde, Department of Marketing and Statistics, Aarhus School of Business, Fuglesangs Allé 4, 8210 Aarhus V, Denmark, or e-mail: alunde{at}asb.dk.

We consider the problem of deriving an empirical measure of daily integrated variance (IV) in the situation where high-frequency price data are unavailable for part of the day. We study three estimators in this context and characterize the assumptions that justify their use. We show that the optimal combination of the realized variance and squared overnight return can be determined, despite the latent nature of IV, and we discuss this result in relation to the problem of combining forecasts. Finally, we apply our theoretical results and construct four years of daily volatility estimates for the 30 stocks of the Dow Jones Industrial Average.

KEYWORDS: high-frequency data, market microstructure noise, realized variance

Received April 1, 2004; revised April 15, 2005; accepted July 20, 2005


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




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.