Modeling conditional return autocorrelation

Michael D. McKenzie*, Robert W. Faff

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 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 languageEnglish
Pages (from-to)23-42
Number of pages20
JournalInternational Review of Financial Analysis
Volume14
Issue number1
DOIs
Publication statusPublished - 2005

Keywords

  • Conditional autocorrelation
  • GARCH

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