TY - JOUR
T1 - The determinants of conditional autocorrelation in stock returns
AU - McKenzie, Michael D.
AU - Faff, Robert W.
N1 - Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2003
Y1 - 2003
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=0142086480&partnerID=8YFLogxK
U2 - 10.1111/1475-6803.00058
DO - 10.1111/1475-6803.00058
M3 - Article
AN - SCOPUS:0142086480
SN - 0270-2592
VL - 26
SP - 259
EP - 274
JO - Journal of Financial Research
JF - Journal of Financial Research
IS - 2
ER -