PREDICTING STOCK INDEX VOLATILITY - CAN MARKET VOLUME HELP

This paper explores a number of statistical models for predicting the daily stock return volatility of an aggregate of all stocks traded on the NYSE. An application of linear and non-linear Granger causality te sts highlights evidence of bidirectional causality, although the relat ionship is stronger from volatility to volume than the other way aroun d. The out-of-sample forecasting performance of various linear, GARCH, EGARCH, GJR and neural network models of volatility are evaluated and compared. The models are also augmented by the addition of a measure of lagged volume to form more general ex-ante forecasting models. The results indicate that augmenting models of volatility with measures of lagged volume leads only to very modest improvements, if any, in fore casting performance. (C) 1998 John Wiley & Sons, Ltd.