Sequential Information Arrival Hypothesis: More Evidence from the Indian Derivatives Market.

By: Material type: ArticleArticlePublication details: Description: 101-110pSubject(s): Online resources: In: Vision:The Journal of Business Perspective, 20 (2) Jun 2016Summary: The study attempts to gather additional evidence on the sequential information arrival hypothesis by examining the dynamic relationship between the trading volume and the volatility of CNX Nifty index futures traded on the National Stock Exchange of India. The documented result using a linear Granger causality model shows a unidirectional causality from volatility to trading volume, thus rejecting the theory of sequential arrival of information in Nifty index futures. Further, under a non-linear GARCH framework of analysis, it is reinforced that a lagged trading volume has no explanatory power of current conditional volatility, even in the presence of market depth surrogated by open interest in the Nifty futures market. Consequently, given the trading volume, the possibility of forecasting the price variability of Nifty futures is rejected.
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The study attempts to gather additional evidence on the sequential information arrival hypothesis by examining the dynamic relationship between the trading volume and the volatility of CNX Nifty index futures traded on the National Stock Exchange of India. The documented result using a linear Granger causality model shows a unidirectional causality from volatility to trading volume, thus rejecting the theory of sequential arrival of information in Nifty index futures. Further, under a non-linear GARCH framework of analysis, it is reinforced that a lagged trading volume has no explanatory power of current conditional volatility, even in the presence of market depth surrogated by open interest in the Nifty futures market. Consequently, given the trading volume, the possibility of forecasting the price variability of Nifty futures is rejected.

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