Modeling conditional return autocorrelation

Michael D. McKenzie; Robert W. Faff

doi:10.1016/j.irfa.2004.06.002

Modeling conditional return autocorrelation

Michael D. McKenzie^*
, Robert W. Faff

^*Corresponding author for this work

Research output: Contribution to journal › Article › Research › peer-review

11 Citations (Scopus)

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.

Original language	English
Pages (from-to)	23-42
Number of pages	20
Journal	International Review of Financial Analysis
Volume	14
Issue number	1
DOIs	https://doi.org/10.1016/j.irfa.2004.06.002
Publication status	Published - 2005
Externally published	Yes

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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.",

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AB - 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.

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Modeling conditional return autocorrelation

Abstract

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