Predicting stock volatility using after-hours information: Evidence from the NASDAQ actively traded stocks
AbstractWe use realized volatilities based on after-hours high frequency stock returns to predict next day stock volatility. We extend the GARCH model to include additional information: the whole after hours period, the preopen realized variance, the postclose realized variance, and the overnight squared return. For the thirty most active NASDAQ stocks, we find that most of the stocks exhibit positive and significant preopen coefficients and that the inclusion of the preopen variance can mostly improve the out-of-sample forecastability of the next day conditional volatility. The inclusions of the postclose variance and overnight squared returns do provide some predictive power for the next day conditional volatility, but to a lesser degree; their predictive abilities are inferior to that of the preopen variance. Our findings support the results of prior studies: traders trade mostly for non-information reasons in the postclose period and trade mostly for information reasons in the preopen period.
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Bibliographic InfoArticle provided by Elsevier in its journal International Journal of Forecasting.
Volume (Year): 28 (2012)
Issue (Month): 2 ()
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Web page: http://www.elsevier.com/locate/ijforecast
Financial markets; GARCH model; Evaluating forecasts; High-frequency data; Realized variance;
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