Predicting stock index volatility: can market volume help?

Brooks, C. ORCID: https://orcid.org/0000-0002-2668-1153 (1998) Predicting stock index volatility: can market volume help? Journal of Forecasting, 17 (1). pp. 59-80. ISSN 1099-131X doi: 10.1002/(SICI)1099-131X(199801)17:1<59::AID-FOR676>3.0.CO;2-H

Abstract/Summary

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
URI	https://reading-clone.eprints-hosting.org/id/eprint/35990
Identification Number/DOI	10.1002/(SICI)1099-131X(199801)17:1<59::AID-FOR676>3.0.CO;2-H
Refereed	Yes
Divisions	Henley Business School > Finance and Accounting
Publisher	Wiley
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