Skip Navigation



Journal of Financial Econometrics Advance Access published online on January 16, 2008
Journal of Financial Econometrics, doi:10.1093/jjfinec/nbm023
This Article
Right arrow Full Text
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
6/2/271    most recent
nbm023v1
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 Raunig, B.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2008. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Detecting ARCH Effects in Non-Gaussian Time Series

Burkhard Raunig
     Oesterreichische Nationalbank

Address correspondence to: Oesterreichische Nationalbank, Economic Studies Division, Otto-Wagner-Platz 3, POB 61, A-1011 Vienna, Austria, or e-mail: burkhard.raunig{at}oenb.co.at

JEL Classification: C12, C14, C22, C52


  Abstract

Engles ARCH test has become the standard test for ARCH effects in applied work. Under non-normality the true rejection probability of this test can differ substantially from the nominal level, however. Bootstrap and Monte Carlo versions of the test may then be used instead. This paper proposes an alternative test procedure. The new test exploits the empirical distribution of the data and an extended probability integral transformation. The test is compared with the former tests in Monte Carlo experiments. Under normality, the new test works as well as the conventional Monte Carlo test and the bootstrap. Under non-normality, the test tends to be more accurate and more powerful than the bootstrapped ARCH test. The procedure is then used to test for ARCH effects in S&P 500 returns sampled at different frequencies. In contrast to the standard and the bootstrapped ARCH tests, the new test detects ARCH effects in the transformed low-frequency returns.

KEYWORDS: ARCH, GARCH, bootstrap, Monte Carlo tests, distributional assumptions

The opinions expressed herein do not necessarily reflect those of the Oesterreichische Nationalbank. I would like to thank the editor, an associate editor, and the anonymous referees. Their comments and suggestions greatly helped to improve the paper.

Received August 23, 2004; revised September 19, 2007; accepted November 20, 2007


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.