Modeling conditional return autocorrelation [An article from: International Review of Financial Analysis]

This digital document is a journal article from International Review of Financial Analysis, published by Elsevier in 2005. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
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.