Can market volume help in predicting share market volatility?

ENGLISH ABSTRACT: This paper explores a number of statistical models for predicting the daily stockreturn volatility of an aggregate of all stocks traded on the Johannesburg StockExchange (JSE). The study is largely inspired by the work of Chris Brooks(1998). The volume of shares traded might be as important as the change in amarket index since substantial price increases and decreases are oftenaccompanied by heavy trading activity. An application of linear and non-linearGranger causality tests highlights evidence of bidirectional causality, although therelationship is stronger from volatility to volume than from volume to volatility.The out-of-sample forecasting performance of various linear and non-linearmodels of volatility are evaluated and compared. The models are alsoaugmented by the addition of a measure of lagged volume to form more generalex-ante forecasting models. The results indicate that augmenting models ofvolatility with measures of lagged volume leads only to fairly small improvementsin forecasting performance. The report also shows that the Johannesburg StockExchange is vulnerable to financial turmoil in other major markets.