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

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  • Sangram K. Jena

Abstract

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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  • Sangram K. Jena, 2016. "Sequential Information Arrival Hypothesis: More Evidence from the Indian Derivatives Market," Vision, , vol. 20(2), pages 101-110, June.
    • Handle: RePEc:sae:vision:v:20:y:2016:i:2:p:101-110
    DOI: 10.1177/0972262916637259
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    References listed on IDEAS

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    Cited by:

    1. Jena, Sangram Keshari & Tiwari, Aviral Kumar & Roubaud, David & Shahbaz, Muhammad, 2018. "Index futures volatility and trading activity: Measuring causality at a multiple horizon," Finance Research Letters, Elsevier, vol. 24(C), pages 247-255.
    2. Iwatsubo, Kentaro & Watkins, Clinton & Xu, Tao, 2018. "Intraday seasonality in efficiency, liquidity, volatility and volume: Platinum and gold futures in Tokyo and New York," Journal of Commodity Markets, Elsevier, vol. 11(C), pages 59-71.

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