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Predicting stock index volatility: can market volume help?

✍ Scribed by Chris Brooks

Publisher: John Wiley and Sons
Year: 1998
Tongue: English
Weight: 246 KB
Volume: 17
Category: Article
ISSN: 0277-6693
DOI: 10.1002/(sici)1099-131x(199801)17:1<59::aid-for676>3.0.co;2-h

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✦ Synopsis

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.

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