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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 effect of trading volume and open interest on volatility of futures markets. The authors capture the size and change in speculative behaviour in futures markets by examining the role of liquidity variables (trading volume and open interest) in the behaviour of futures prices.

The sample includes daily data covering the period 1996-2014 from 36 international futures markets (including currencies, commodities, stock indices, interest rates and bonds). The authors employ a two-stage estimation methodology: first, the authors employ a E-GARCH model and consider the asymmetric response of volatility to shocks of different sign. Further, the authors consider a regression framework to examine the contemporaneous relationships between volatility, trading volume and open interest. To quantify the percentage of volatility that is caused by liquidity variables, the authors also regress the estimated volatilities on the measures of open interest and trading volume.

The authors find that: market depth has an effect on the volatility of futures markets but the direction of this effect depends on the type of contract, and there is evidence of a positive contemporaneous relationship between trading volume and futures volatility for all futures contracts. Impulse-response functions also show that trading volume has a more relevant role in explaining market volatility than open interest.

These results are recommended to financial managers and analysts dealing with futures markets.

To the best of the authors’ knowledge, no study has yet considered a complete database of futures markets to investigate the empirical relation between price changes (volatility), trading volume and open interest in futures markets.

The purpose of this paper is to study the effect of different parts (predictable and impact) of different types of speculative behavior (intraday speculation, medium-term speculation and long-term speculation) on future fluctuations in the underlying index.

The authors input information about heterogeneous speculative behavior into the HAR-RV model to study the effect of different parts (predictable and impact) of different types of speculative behavior (intraday speculation, medium-term speculation and long-term speculation) on the future fluctuation of the underlying index.

The authors find that the increase in intraday speculation will exacerbate spot market volatility; and the expected increase of long-term value speculation can reduce market volatility, but the shock of speculation will exacerbate market volatility.

The authors suggest that regulators should strictly limit speculative intraday trading, and also focus on the long-term value speculation that decreases market volatility, in order to guide the benign development of the markets that stabilize abnormal market fluctuations.

First, in view of the correlation between the futures and spot markets, the authors put forward a new proxy for the speculation degree. Second, the authors input heterogeneous speculative behavior into the HAR-RV model to study the effects of different parts (predictable and impact) on different types of speculative behavior (intraday speculation, medium-term speculation and long-term speculation) on the future fluctuation of the underlying index.

This study explores the price linkage between the Chinese commodity futures market and other dominant futures markets, and examines the forces behind the price linkages. The contribution by the trading hour innovations in the United States (or United Kingdom) market to the overnight price changes in the Chinese market is larger in scale than the contribution by the daytime information from the Chinese market to the overnight returns of the corresponding US (or UK) market. Several futures have significant interactions of the domestic and foreign factors in the price linkages while the Chinese domestic factors explain better the global market price linkage in some futures (aluminum, gold, and corn), demonstrating the leading role of the Chinese futures markets in these world markets.

Guar Seed crop is ruling the Indian International business mainly due to its application as a drilling fluid in shale energy industry concentrated in the USA. One of the allegations against futures market is its possible role in increasing the volatility of underlying physical market prices. Suspension of guar seed futures contract in 2012 at National Commodity Derivatives Exchange of India (NCDEX)-India, has reignited the controversy and raised an alarm bell to peek into obscure world of Indian commodity derivatives market. Against the backdrop of fiasco in guar futures trading, the purpose of this paper is to investigate whether sudden surge in futures trading volume leads to increase in the volatility of spot market prices.

Guar seed spot returns volatility is modeled as a GARCH (1, 1) process. Futures trading volume and open interest are segregated into expected and unexpected components. The data are analyzed from 2004 to 2011 using Augmented GARCH model to study the contemporaneous relationship between spot volatility and unexpected futures trading activity and Granger Causality test for examining the dynamic relationship between them and ascertaining causality.

Augmented GARCH model reports positive relationship between unexpected futures trading volume (UTV) and spot returns volatility, and, Granger Causality flows from UTV to spot volatility. Therefore, when the level of futures trading volume increases unexpectedly, the volatility of spot prices increases pointing toward the destabilizing impact of futures trading. However, hedger’s activity, represented by open interest is not seen to have any causal/destabilizing impact on spot price volatility of guar seed.

The study provides empirical evidence to support the concern of regulators, genuine hedgers and other traders about the presence of excessive speculation and market manipulations perpetrated through futures market that is disturbing the underlying physical market instead of strengthening it by aiding in price discovery and risk mitigation.

There are very few studies which have empirically investigated the temporal relation between volume and volatility in Indian agricultural commodity markets. With guar seed as a special case the present study investigates statistically the impact of futures trading on spot price volatility. In light of the findings of the study, the curb imposed on guar seed futures trading in 2012 was justified.

The purpose of this paper is to examine the contemporaneous and causal relationship between returns (volatility) and trading volume in the Indian currency futures market for selected currency pairs; USD-INR, EUR-INR, GBP-INR and JPY-INR, from August 2008 to December 2014.

The data for all the currency futures series has been taken from National Stock Exchange of India Limited which represents the daily settlement prices along with trading volume. The contemporaneous returns-volume relation is tested using the generalized method of moments, and Granger-causality framework impulse response function is used to test the predictive ability of returns (volatility) and volume for each other.

The author reports a positive contemporaneous relationship between futures returns and trading volume which persists even after controlling for heteroskedasticity providing support to mixture of distribution hypothesis. The results show a unidirectional Granger causality from futures returns to volume. However, there is a significant bidirectional Granger causality between returns volatility and volume lending support to sequential arrival of information hypothesis. Next, the results for cross-currencies show significant influence of US dollar on the volume and returns of all other currencies. Overall, the author suggests that the short- to medium-term movements in the currency markets are dominated by market microstructure and not by fundamentals.

The findings of this paper are very important for the participants in the market and regulators. The participants in the market require alternatives to diversify their risk. The significant relationship between futures returns (volatility) and trading volume implies that the current trading volume help predict the futures prices and should lead to creation of more reliable hedging strategies for investment purposes. Further, it may interest the regulators who need to decide upon the appropriateness of their policies in the currency futures market. Based on returns-volume relation, they need to set forth market restrictions such as daily price movement and position limits.

To the best of the knowledge, no study has yet investigated the forecast ability of trading volume to price changes and their volatility in the Indian 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 relationship in the Indian currency futures market.

The purpose of this paper is to examine whether the futures volatility could affect the investor behavior and what trading strategy different investors could adopt when they meet different information conditions.

This study introduces a two‐period overlapping generation model (OLG) model into the future market and set the investor behavior model based on the future contract price, which can also be extended to complete and incomplete information. It provides the equilibrium solution and uses cuprum tick data in SHFE to conduct the empirical analysis.

The two‐period OLG model based on the future market is consistent with the practical situation; second, the sufficient information investors such as institutional adopt reversal trading patterns generally; last, the insufficient information investors such as individual investors adopt momentum trading patterns in general.

Investor trading behavior is always an important issue in the behavioral finance and market supervision, but the related research is scarce.

The conclusion shows that the investors' behavior in Chinese future market is different from the Chinese stock market.

This study empirically analyzes and verifies the different types of trading strategies investors could; investors such as institutional ones adopt reversal trading patterns generally; while investors such as individual investors adopt momentum trading patterns in general.

For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

In this paper we conduct tests for two different trading rules, namely, the Dual Moving Average (DMA) model and the Channel Breakout (CHB) rule. These rules are tested across five futures contracts – the S&P 500, British Pound, US T‐Bonds, COMEX Gold and Corn using daily data over the period 1990 to 1998. Overwhelmingly, we find that the trading rules are unable to produce (gross or net) profits at any statistical level. While positive gross and net profits were available in four of the five markets, the profits were neither economically or statistically significant.