This article investigates the role of price discovery in the Indian stock market by taking into consideration 41 individual securities and Nifty. The present article reports a study based on the daily adjusted closing price of spot and futures for the time period between June 2000 and August 2010 (index) and from November 2001 to August 2010 (individual stocks). To analyze the price discovery role, Engle and Granger’s Residual Based approach, Johansen’s cointegration test and VECM (Vector Error Correction Model) are used. The results depict that futures price series leads the spot price series in case of Nifty and 21 stocks, and 20 stocks’ future price series is led by spot price series. As far as causality is concerned, 14 stocks show feedback causality, 21 stocks including Nifty show unilateral causality and 7 stocks show absence of causality. This study also finds that both the markets, i.e., futures and spot, play an important price-discovery role, implying that futures (spot) prices may contain useful information about spot (futures) prices.
The present study aims to investigate the presence or absence of weak form market efficiency and unriddle the potential factors impacting the chaotic pattern of the stock market. The study carries the analysis by considering 12 countries' indices categorized as developing and developed on the basis of their GDP. Five econometric tools were applied for accomplishing the objectives and it was evidenced that the American and Indian stock market are weak-form inefficient whereas most of the statistical tools adjudged three countries (i.e., Hong Kong, Singapore & South Korea) weak-form efficient. It was also unveiled in the study that settlement cycle, information disclosure, thinness of trading, trading hours, and market size could be the potential reasons impacting the weak form of efficiency of the stock market.
This study aims to examine the changes in the dynamic relationship between return registered by stocks and trading volume because of the changes in the flow of Foreign Institutional Investments in the Indian stock market. Author has tested the relationship using daily data of S&P CNX Nifty (that is, NSE’s index) from 2000 to 2013 and methodologies described by Bollerslav (1986), Engle (1982) and Sims (1980), that is, Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) (1, 1), Exponential Generalized Auto Regressive Conditional Heteroskedasticity (EGARCH) (1, 1), Vector Autoregression (VAR), Granger causality, Variance Decomposition (VDC) and Impulse Response Function (IRF). The empirical analysis evidences the significant role of trading volume in lessening volatility and also adjudges the Indian stock market highly inefficient due to the presence of volatility persistence. In addition, VAR results document serial correlation between trading volume coefficients and its lagged values in case of all the subsamples that confirm the flow of information arrival sequential. Besides this, VDC and IRF results also assert that the amount of information contributed by stock return for the prediction of trading volume is far greater than the other way around and announce the study of stock return more useful in predicting the future kinetics of stock return as well as trading volume.
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