Modeling conditional return autocorrelation

McKenzie, Michael D. and Faff, Robert W. (2005) Modeling conditional return autocorrelation. International Review of Financial Analysis, 14 1: 23-42. doi:10.1016/j.irfa.2004.06.002

Author McKenzie, Michael D.
Faff, Robert W.
Title Modeling conditional return autocorrelation
Journal name International Review of Financial Analysis   Check publisher's open access policy
ISSN 1873-8079
Publication date 2005
Year available 2004
Sub-type Article (original research)
DOI 10.1016/j.irfa.2004.06.002
Volume 14
Issue 1
Start page 23
End page 42
Total pages 20
Place of publication Amsterdam, Netherlands
Publisher Elsevier BV
Language eng
Abstract Empirical estimates of conditional return autocorrelation are generated over the period 1973 to 2000 for S&P500 index data, as well as for a small selection of individual U.S. stocks. We find that conditional autocorrelation is highly variable, and these dynamics are consistent with changes in point autocorrelation estimates generated in various subperiods. The conditional autocorrelation estimates for some stocks exhibited a pattern of mean reversion, while for others, evidence of long-term trends and structural breaks was found. While we were unable to uncover what characteristics drive the nature of these autocorrelation patterns, our analysis ruled out industry, investor type or degree of internationalisation as explanations.
Keyword GARCH
Conditional autocorrelation
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ
Additional Notes Available online 2 July 2004

Document type: Journal Article
Sub-type: Article (original research)
Collection: UQ Business School Publications
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Created: Tue, 08 Mar 2011, 13:08:09 EST by Karen Morgan on behalf of UQ Business School