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The purpose of the paper is to study the explainability of expected and unexpected trade volume and open interest as information flow, and the asymmetric effects of unexpected shocks to the information flow on volatility in Indian commodity markets.

After having dissected into expected and unexpected components, the effects of trade volume and open interest on volatility are tested. A new interaction term is also added to measure asymmetry. Four commodities, namely, cumin, soy oil and pepper in food commodity category and guar seed in non-food commodity category are selected for the present study. These four commodities are selected based on their economic and trading importance, i.e. weight in the index and trading volume (liquidity).

It is mostly found that unexpected volatility is positively related to the volatility, and the effect of the unexpected component is more than the expected component of the trading volume. The expected open interest is negatively related to volatility while the unexpected open interest is found to be positively related in all the commodities. The effects of unexpected component are higher than the expected open interest. The effects of positive unexpected shocks to the trade volume are more than those of negative unexpected shocks. The evidence of asymmetry in unexpected shocks to open interest is inconclusive. However, the inclusion of volume of trade and open interest could not vanish away the volatility. This indicates that the trading volume and open interest are not the variable with mixed distribution. Thus, it contrasts the assumption of mixed distribution hypothesis, and they do not proxy the flow of information.

It is the unexpected information flow that matters more than the expected one. Positive unexpected shocks to trade volume are more influential than the negative shocks. However, trade volume and open interest are not good proxy of information flow in the Indian commodity markets. This study would definitely broaden the horizon of managers and policymakers to understand the volatility better.

The paper is unique in terms of understanding the effect of expected and unexpected trade volume and open interest and the asymmetric effects of unexpected shocks to volume and open interest in the Indian commodity markets.

The purpose of this study is to examine the impact of the trade-activities in option market on stock market volatility by using KOSPI 200 daily data. First. we divided the option Volume from open interest. then classify the unexpected volume from expected volume to group the trade-activities In option market according to Investor's type. The result of test find that the unexpected volume of call option and the stock volatility have positive relationship. while one of put option and the stock volatility do not have statistically significant relation.

Then. we also divide the option volume Into the classified option volume according to time-maturity. We expect that the informed trader and uninformed trader trade the classified option differently. As a result. in the case of the call ootlon. the trade-activity of the unexpected volume in deep in the money option and deep out 이 the money option has positive relation with the stock volatility. and the at the money option below a month to maturity has positive relation with the stock volatility. This result shows that the informed trader prefer deep in the money option. deep out of the money option and at the money option below a month to maturity among the various option series. But. In the case of put option, it is so hard to find the result of the Informed trader's behavior.

The purpose of this paper is to identify the effective measures for heterogeneity and to uncover the relationship between investor heterogeneity and stock returns.

The paper employs dispersion in analysts’ earnings forecasts and unexpected trading volume as proxies of heterogeneity. Portfolio strategies and Fama-Macbeth regression are used to uncover the relationship between the two proxies and stock returns in the Chinese A-share market.

The result indicates that stock returns are significantly related to unexpected trading volume, i.e., higher unexpected trading volume implies higher stock returns now but lower future stock returns. In contrast, there is no statistically significant relationship between analysts’ forecast dispersion and stock returns.

The findings suggest that unexplained trading volume is an effective measure for investor heterogeneity in the Chinese A-share market.

The purpose of this paper is to examine the relationship between trading volume and returns in the West Texas Intermediate (WTI) and Brent crude oil futures markets. In so doing, the paper addresses two important issues. First, whether there is a positive relationship between returns and trading volume in the crude oil futures markets. Second, whether information regarding trading volume contributes to forecasting the magnitude of return in the markets, an important issue because the ability of trading volume to predict returns imply market inefficiency.

The paper used daily closing futures price and their corresponding trading volumes for WTI and Brent crude oil markets during the sample period January 2008 to May 2011. Both the log volume and the unexpected component of the detrended volume are used in the analysis in other to have robust alternative conclusion. The generalized method of moments (GMM) approach is used to examine the contemporaneous relationship between returns and trading volume while the Granger causality approach, impulse response and variance decomposition analysis are used to investigate the ability of trading volume to predict returns in the oil futures markets.

The results reject the postulation of a positive relationship between trading volume and returns, suggesting that trading volume and returns are not driven by the same information flow which contradicts the mixture of distribution hypothesis in all markets. The results also show that neither trading volume nor returns have the power to predict the other and therefore contradicting the sequential arrival hypothesis and noise trader model in all markets. Finally, the findings support the weak form efficient market hypothesis in the crude oil futures markets.

The findings has important implications to market regulators because daily price movement and trading volume do not respond to the same information flow and therefore the measures that control price volatility should not focused more on volume; otherwise they may not provide fruitful outcomes. Additionally, traders and investors who participate in oil futures should not base their decisions on past trading volume because it will lead to profit loss. The results also have implications for market efficiency as past information cannot assist speculators to forecast returns in all the oil markets. Finally, investors can benefit from portfolio diversification across the two markets.

The purpose of this paper is to examine the change in speed of dissemination of order flow information on stock volatility of return in 79 traded companies at the Cairo and Alexandria Stock Exchange (CASE).

The paper examines the interaction of volatility and volume in 79 traded companies in CASE over a period from January 1998 to May 2005 and provides support for the TGARCH specification for explaining the daily time dependence on the rate of information arrival to the market for stocks traded on CASE.

The paper finds that information size and direction have a negligible effect on conditional volatility and, as a result, the presence of noise trading and speculative bubbles is suspected. It was found that the persistence in volatility is not eliminated when lagged or contemporaneous trading volume is incorporated into a GARCH model. It is shown that, when volume is further broken down into its expected and unexpected components, volatility persistence decreases. This is especially true after May 2001, which marks the beginning of a succession of major stock market reforms. It was also found that anticipated information shocks can have a negative impact on the volatility of return, particularly prior to May 2001.

The decrease in the negative relationship between expected volume and volatility after May 2001 suggests that trading efficiency and information dissemination have improved. This is an important finding for CASE as it encourages the reform momentum and reinsures foreign investors.

The purpose of this paper is twofold. The first is to estimate the correlation between market activity and volatility on an exchange that does not use continuous auctions to find prices. The second is to estimate the sensitivity of that relationship to differences in opinions across traders regarding asset value.

Both objectives are accomplished by using seven years of trader‐level data from the Tokyo Grain Exchange, which uses rapid sequences of Walrasian tâtonnement auctions to discover prices. On the TGE, only one futures contract trades at any given time and all of a commodity's futures contracts are auctioned in a rapid sequence, with only seconds between a sequence's auctions. The results are interpreted under the hypothesis that this design causes traders' beliefs to become more accurate and more uniform as a sequence progresses.

Intraday volume is u‐shaped while intraday volatility is downward sloping. The volume–volatility link is positive and stays constant or strengthens as traders' beliefs about value become more precise. The link is driven by trades originating from small futures commission merchants, especially those trades entered on behalf of customers.

Evidence that accounting for cross‐correlations when estimating volatility can have an important effect on estimates is presented. Researchers are encouraged to further explore the implications of cross‐correlations.

The paper includes implications for existing theory, the measurement of volatility, and the design of central exchanges.

This paper uses the TGE as a natural laboratory to test theory. It is the first such study to use data from an exchange that does not use continuous auctions, and the first to document the simultaneous existence of u‐shape volume and downward‐sloping volatility.

The purpose of this paper is to propose a novel and new direct measurement of small investor sentiment in the equity market. The sentiment is based on the individual investors’ internet search activity.

The author measures unexpected changes in the small investor sentiment with AR (1) process, where the residuals capture the unexpected changes in small investor sentiment. The author employs vector autoregressive, Granger causality and linear regression models to estimate the association between the unexpected changes in small investor sentiment and future equity market returns.

An unexpected increase in the search popularity of the term bear market is negatively associated with the following week’s equity market returns. An unexpected increase in the spread (the difference in popularities between a bull market and a bear market) is positively associated with the following week’s equity market returns. The author finds that these effects are stronger for small-sized companies.

By author’s knowledge, the paper is the first that measures the small investor sentiment that is based on the internet search activity for keywords used in the American Association of Individual Investor’s (AAII) survey questions. The paper proposes an alternative small investor sentiment measure that captures the changes in small investor sentiment in more timely fashion than the AAII survey.

The purpose of this paper is to ascertain the possible consequences of ban on futures trading of agriculture commodities in India by examining three critical issues: first, the author explores whether price discovery dominance changes between futures and spot in the pre-ban and post-relaunch phase both in the long run and short run. Second, the author examines the impact of ban and relaunch of futures trading on its underlying spot volatility for five sample cases of agriculture commodities (Wheat, Sugar, Soya Refined Oil, Rubber and Chana) using both parametric and non-parametric tests. Third, the author revisits the destabilization hypothesis in the light of ban on futures trading by examining the impact of unexpected component of liquidity of futures on spot volatility.

The author uses widely adopted methodology of co-integration to examine long-run relationship between spot and futures, while the short-run relationship is investigated using vector error correction model (VECM) and Granger causality to test price discovery in the pre-ban and post-relaunch phases. The second objective is explored using a combination of parametric and non-parametric tests such as Welch one-way ANOVA and Kruskal–Wallis test, respectively, to gauge the impact of ban on futures trading on spot volatility along with post hoc tests to investigate pairwise comparison of spot volatility among three phases (pre-ban, ban and post-relaunch) using Dunn Test. In addition, extensive robustness test is undertaken by adopting augmented E-GARCH model to ascertain the impact of ban and relaunch of futures trading on spot volatility. The third objective is investigated using Granger causality test between spot volatility and unexpected component of liquidity of futures estimated using Hodrick and Prescott (HP) filter to re-visit the destabilization hypothesis.

The author found extensive evidence for the dominance of futures market in the price discovery of agriculture commodities both in the pre-ban and post-relaunch phases in India. The ban on futures trading is found to have a destabilizing impact on spot volatility as evident from the findings of Wheat, Sugar and Rubber. In addition, it is observed that spot volatility was highest during the ban phase as compared to the pre-ban and post-relaunch phases for all four commodities barring Chana. The author found that destabilisation hypothesis holds true during the pre ban phase, while weakening of destabilization hypothesis is observed in the post-relaunch phase as unexpected futures liquidity has no role in driving the spot volatility.

This study is a novel attempt to empirically examine the potential impact of ban and relaunch of futures trading of agriculture commodities on two key market quality dimensions – price discovery and spot volatility. In addition, destabilization hypothesis is revisited to investigate the impact of futures trading on spot volatility during the pre-ban and post-relaunch period.

The purpose of this paper is to examine the linear and nonlinear relations between returns volatility and trading volume for the Indian currency futures market.

To examine the contemporaneous relation between returns volatility and volume, the author uses the generalized method of moment estimator. For the linear causal relation, the author makes use of Granger (1969) bivariate vector autoregression model. The author tests for nonlinear Granger causality between returns volatility and trading volume based on a modified version of the Baek and Brock (1992) nonparametric technique developed by Hiemstra and Jones (1994).

The results indicate a negative contemporaneous relation between returns volatility and trading volume; therefore, the mixture of distribution hypothesis is not supported. The results of both linear and nonlinear Granger causality between futures returns volatility and trading volume indicate a significant bidirectional relation between the two variables lending support to the sequential arrival of information hypothesis. The results are robust to divergence of opinions as proxied by open interest.

The findings of this paper are important for the participants in the market and regulators. The participants in the market require alternatives to diversify their risk. The significant causal relation between returns volatility and trading volume implies that trading volume helps predict the futures prices and should lead to creation of more reliable hedging strategies for investment purposes. Furthermore, it may interest the regulators who need to decide upon the appropriateness of their policies in the currency futures market.

To the best of the author’s knowledge, there is no study that investigates the forecast ability of trading volume to futures returns volatility in an emerging currency futures market. Given that currency futures market is one of the largest markets in the world, and Indian rupee has seen wide fluctuations in the recent years, it seems exciting to explore the price–volume relation in the Indian currency futures market.

The purpose of our study is to explore the “idiosyncratic volatility puzzle” in Chinese stock market from the perspective of investors' heterogeneous beliefs. To delve into the relationship between idiosyncratic volatility and investors' heterogeneous beliefs, and uncover the ability of heterogeneous beliefs, as well as to explain the “idiosyncratic volatility puzzle”, we construct our study as follows.

Our study adopts the unexpected trading volume as proxies of heterogeneity, the residual of Fama–French three-factor model as proxies of idiosyncratic volatility. Portfolio strategies and Fama–MacBeth regression are used to investigate the relationship between the two proxies and stock returns in Chinese A-share market.

Investors' heterogeneous beliefs, as an intermediary variable, are positively correlated with idiosyncratic volatility. Meanwhile, it could better demonstrate the negative correlation between the idiosyncratic volatility and future stock returns. It is one of the economic mechanisms linking idiosyncratic volatility to subsequent stock returns, which can account for 11.28% of the puzzle.

The findings indicate that idiosyncratic volatility is significantly and positively correlated with heterogeneous beliefs and that heterogeneous beliefs are effective intervening variables to explain the “idiosyncratic volatility puzzle”.