TY - JOUR
T1 - Modeling conditional return autocorrelation
AU - McKenzie, Michael D.
AU - Faff, Robert W.
N1 - Copyright:
Copyright 2005 Elsevier B.V., All rights reserved.
PY - 2005
Y1 - 2005
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=11444267166&partnerID=8YFLogxK
U2 - 10.1016/j.irfa.2004.06.002
DO - 10.1016/j.irfa.2004.06.002
M3 - Article
AN - SCOPUS:11444267166
SN - 1057-5219
VL - 14
SP - 23
EP - 42
JO - International Review of Financial Analysis
JF - International Review of Financial Analysis
IS - 1
ER -