Using Granger Causal and Cointegration Tests, we analyzed the relationship between the open interests of commercial investors, non-commercial invest institution, retail dealers and the oil price in WTI futures market from 2003 to 2012. Found the short positions of commercial investors are determined by the basis and futures price. And the long positions held by commercial investors are for the need of commercial investors and invest institution to hold short positions. The investment institutions adjust the long positions by the change of the futures price. There are the co-integration relationship between the positions hold by Commercial customers and investment institutions. And the changes of short positions hold by investment institutions are affected by the long positions hold by investment institutions. The long positions holds by the commercial investors are affected by the short positions hold by the commercial investors and investment institutions. These founding can help us to understand the relationship and internal influences mechanism between the open interests and price in futures markets.

We compare the effects of futures market margin policy on trading activity and volatility between the China margin system and the U.S. margin system. In China margin levels are set as a fixed percentage of the underlying futures contract value and change daily as futures prices change over time. In contrast, margin is set at a fixed dollar amount for most contracts in the United States and is infrequently adjusted at the discretion of the exchange’s clearinghouse. We provide a theoretic model on how the changing margin cost between market-up days and market-down days would affect the demand and supply of short term speculators and long term hedgers in the Chinese futures market and their different effects on market volatility. The model shows that the futures price shocks should have an asymmetric effect on trading volume and volatility in the Chinese market but symmetric effect in the U.S. market and futures price should have a return dynamics that is more stable in the Chinese market than in the U.S. market. Using Soybean futures data from the Chinese and U.S. markets, we compare price and volatility dynamics between the two markets and find empirical support for our theoretic model and hypothesis.

As the launch of the China’s first stock index futures (SIF) approaches with no exact date for its eventual introduction. The Chinese stock market has increased dramatically due to this expectation recently, especially the futures contracts related stocks have raised significantly which are good examples of this influence. As the stock index futures is a new financial product, Chinese investors cannot help wondering whether the launch of the stock index future will have a positive or negative impact upon the underlying stock market. On the other hand, the new instruments which, will be followed by the introduction of other derivatives, will require broker-dealers to upgrade their systems and invest in new technology. Therefore, it has become pertinent to investigate the opportunities and challenges this eagerly awaited derivative instrument has to offer to fund managers in the booming Chinese economy.

This paper investigates the hedging effectiveness of the Copper Futures contracts using daily settlement prices for the period from 23 July, 2008 to 3 July, 2009. Different econometric models are used to estimate the optimal hedging ratios of Copper Futures on the Shanghai Futures Market. The hedging performance is firstly analyzed by the OLS regression model, the Error Correction model (ECM) and the Bivariate-GARCH Model. Then the Minimum-Variance Hedge Strategy is adopted to evaluate the statistical models. Secondly this research uses a non-parametrical method, the Genetic Algorithms to predict the hedging ratio based on the historical data. Then finally whether the Genetic Programming could produce better hedging parameters than the standard hedging model will be revealed.

This paper investigates information transmission and price discovery mechanisms in informationally linked and non-synchronous trading markets within the multivariate generalized autoregressive conditional heteroskedasticity framework. Using daily data for copper and soybean contracts from the Chinese futures and spot markets, as well as the London Metal Exchange (LME) and Chicago Board of Trade (CBOT) futures markets, we show that there are asymmetric lead-lag relationships between any two of the three markets. We also find that the volatilities spill over from one market to another for both cases of copper and soybeans. However, the copper and soybean markets exhibit quite different patterns of information transmission. Further, we highlight the remarkable role of the Chinese futures markets in the price formation process, though the LME/CBOT futures markets are the main driving force in price discovery.

This paper provides a theoretical analysis of return and volatility spillover effects between developed and emerging futures markets with investment obstacles. It mainly focuses on analysis of the effects on equilibrium futures price, investors’ trading strategies and their wealth distributions in the emerging market. Three hypotheses are proposed. The first two assume that there is either return or volatility spillover between the two markets. The last one combines the first two together by assuming that there are both return and volatility spillovers between the markets. Our analysis results show that the equilibrium futures price, investors’ trading strategies and their wealth distributions in the emerging futures market are affected by (1) the scale of informed traders in the emerging market who form their expectations of delivery price by using the spillovers from the developed market, (2) the spillovers degree that the informed in the emerging market expect, and (3) whether there is return spillover or volatility spillover, or both. Overall, the findings suggest that if there are both return and volatility spillovers, then ignoring the volatility spillover, investors will make improper investment decisions so that the futures contracts could be overpriced and the traders’ wealth could be harmed. The theoretical analysis provide an important implication for empirical examination on the spillover effects between markets, that is, both return and volatility spillover effects should be considered jointly, otherwise the return spillover effects can be overestimated. Empirical examination in copper futures markets generally supports the conclusions drawn from our theoretical analysis.

This study estimates optimal hedge ratios using various econometric models. These models are evaluated based on the in- and out-of-sample optimal hedge ratio forecasts. Using daily data of spot and futures 1-month, 3-month, 6-month prices of aluminum and copper in the Shanghai Futures Exchange, the optimal hedge ratios are calculated from the OLS regression model, the VAR with error correction model, the bivariate GARCH model and the Exponential GARCH (EGARCH) Model. Hedging performance in terms of variance reduction of returns from four different models are also conducted. It is found that the EGARCH hedge ratio provides the largest reduction in the variance of the return portfolio, but they do not perform better than the alternatives over the out-of-sample period.