The determinants of conditional autocorrelation in stock returns

Michael D. McKenzie, Robert W. Faff

Research output: Contribution to journalArticle

27 Citations (Scopus)

Abstract

We investigate whether return volatility, trading volume, return asymmetry, business cycles, and day-of-the-week are potential determinants of conditional auto-correlation in stock returns. Our primary focus is on the role of feedback trading and the interplay of return volatility. We present empirical evidence using conditional autocorrelation estimates generated from multivariate generalized autoregressive conditional heteroskedasticity (M-GARCH) models for individual U.S. stock and index data. In addition to return volatility, we find that trading volume and market returns are important in explaining the time-varying patterns of return autocorrelation.

Original languageEnglish
Pages (from-to)259-274
Number of pages16
JournalJournal of Financial Research
Volume26
Issue number2
DOIs
Publication statusPublished - 2003

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