Generalized variance ratio tests in the presence of statistical dependence

Periklis Kougoulis, John C. Nankervis and Jerry Coakley
Additional contact information
John C. Nankervis: University of Essex

No 180, Computing in Economics and Finance 2006 from Society for Computational Economics

Abstract: We develop extensions of the variance-ratio statistic for testing the hypothesis a time series is uncorrelated and investigate their finite-sample performance. The tests employ an estimator of the asymptotic covariance matrix of the sample autocorrelations that is consistent under the null for general classes of innovations including EGARCH and non-MDS processes. Monte Carlo experiments show that our tests have better finite-sample size and power properties than the standard variance-ratio tests in experiments using time series generated by EGARCH and non-MDS processes

Keywords: Non-MDS process; Monte Carlo (search for similar items in EconPapers)
JEL-codes: C14 C15 C22 (search for similar items in EconPapers)
Date: 2006-07-04
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Journal Article: Generalized Variance-Ratio Tests in the Presence of Statistical Dependence (2015)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sce:scecfa:180

Access Statistics for this paper

More papers in Computing in Economics and Finance 2006 from Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F. Baum ().