This paper provides empirical support for the notion that Autoregressive Conditional Heteroskedasticity (ARCH) in daily stock return data reflects time dependence in the process generating information flow to the market. Daily trading volume, used as a proxy for information arrival time, is shown to have significant explanatory power regarding the variance of daily returns, which is an implication of the assumption that daily returns are subordinated to intraday equilibrium returns. Furthermore, ARCH effects tend to disappear when volume is included in the variance equation.

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. American Statistical Association is collaborating with JSTOR to digitize, preserve and extend access to Journal of Business &Economic Statistics. This article examines the persistence of the variance, as measured by the generalized autoregressive conditional heteroskedasticity (GARCH) model, in stock-return data. In particular, we investigate the extent to which persistence in variance may be overstated because of the existence of, and failure to take account of, deterministic structural shifts in the model. Both an analysis of daily stock-return data and a Monte Carlo simulation experiment confirm the hypothesis that GARCH measures of persistence in variance are sensitive to this type of model misspecification.

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