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.
AkgirayV., BoothG. C., HatemJ. J., & MustafaC. (1991). Conditional dependence in precious metal prices. The Financial Review, 26(August), 367–386.
2.
BoardJ. L., & SutcliffeC. M. S. (1990). Information, volatility, volume and maturity: An investigation of stock index futures. Review of Futures Markets, 9(3), 191–210.
3.
BollerslevT. (1987). A conditionally heteroskedastic time series model for speculative prices and rates of return. The Review of Economics and Statistics, 69(3), 542–547.
4.
BollerslevT., ChouR., & KronerK. (1992) ARCH modelling in finance. Journal of Econometrics, 52(1/2), 5–59.
5.
ClarkP. K. (1973). A subordinated stochastic process model with finite variance for speculative prices. Econometrica, 41(1), 135–155.
6.
CopelandT. E. (1976). A model of asset trading under the assumption of sequential information arrival. Journal of Finance, 31(4), 1149–1168.
7.
CornellB. (1981). The relationship between volume and price variability in futures market. Journal of Futures Market, 1(3), 303–316.
8.
DickeyD. A., & FullerW. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427–431.
9.
EngleR. F. (1982). Autoregressive Conditional Heteroskedasticity with estimates of the variance of UK inflation. Econometrica. 50(4), 987–1008.
10.
EngleR., & BollerslevT. (1986). Modelling the persistence of conditional variances. Econometric Reviews, 5(1), 1–50.
11.
EppsT. W., & EppsM. L. (1976). The stochastic dependence of security price changes and transaction volumes: Implication for mixture of-distributions hypothesis. Econometrica, 44, 305–321.
12.
FujiharaR. A., & MougoueM. (1997). Linear dependence, nonlinear dependence and petroleum futures market efficiency. Journal of Futures Markets, 17(1), 75–99.
13.
GallantA. R., RossiP. E., & TauchenG. (1992). Stock prices and volume. Review of Financial Studies, 5(2), 199–242.
14.
GrammatikosT., & SaundersA. (1986). Futures’ price variability: A test of maturity and volume effect. Journal of Business, 59(2), 309–330.
15.
GrangerC. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424–438.
16.
HarrisL. E. (1987). Transaction data tests of the mixture of distributions hypothesis. Journal of Financial and Quantitative Analysis, 22(2), 127–141.
17.
JenningsR. H., StarksL. T., & FellinghamJ. C. (1981). An equilibrium model of asset trading with sequential information arrival. Journal of Finance, 36(1), 143–161.
18.
KalotychouE, & StaikourasS. K. (2006). Volatility and trading activity in Short Sterling futures. Applied Economics, 38(9), 997–1005.
19.
KarpoffJ. M. (1987). The relation between price changes and trading volume: A survey. Journal of Financial and Quantitative Analysis, 22(1), 109–126.
20.
KawallerI. G., KochP. D., & KochT. W. (1987). The temporal price relationship between S&P 500 Futures and the S&P Index. Journal of Finance, 42(5), 1309–1329.
21.
KutanA. M., & AksoyT. (2003). Public information arrival and the Fisher effect in emerging market: Evidence from stock and bond market in Turkey. Journal of Financial Services Research, 23(3), 225–239.
22.
KyleA. (1985). Continuous auctions and insider trading. Econometrica, 53(6), 1315–1335.
23.
LamoureuxC., & LatrapesW. (1990). Heteroskedasticity in stock return data: Volume versus GARCH effects. Journal of Finance45(1), 221–229.
24.
MougoueM., & AggarwalR. (2011). Trading volume and exchange rate volatility: Evidence for the sequential arrival of information hypothesis. Journal of Bank Finance, 35(10), 2690–2703.
25.
NajandM., & YungK. (1991). A GARCH examination of the relationship between volume and price variability in futures markets. Journal of Futures Markets, 1(5), 613–621.
26.
SmirlockM., & StarksL. (1988). An empirical analysis of the stock price-volume relationship. Journal of Banking and Finance, 12(1), 31–41
27.
TauchenG. E., & PittsM. (1983). The price variability-volume relationship on speculative markets. Econometrica, 51(2), 485–505.
