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Predicting stock index volatility: can market volume help?

Brooks, C. ORCID: (1998) Predicting stock index volatility: can market volume help? Journal of Forecasting, 17 (1). pp. 59-80. ISSN 1099-131X

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To link to this item DOI: 10.1002/(SICI)1099-131X(199801)17:1<59::AID-FOR676>3.0.CO;2-H


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 tests highlights evidence of bidirectional causality, although the relationship is stronger from volatility to volume than the other way around. 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 forecasting performance.

Item Type:Article
Divisions:Henley Business School > ICMA Centre
ID Code:35990


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