Trading Activities and the Volatility of Return on Malaysian Crude Palm Oil Futures

Trading activities represent the flow of market information to the investors. This paper examines the effect of trading activities, i.e., trading volume and open interest, on the volatility of return for Malaysian Crude Palm Oil Futures. The GARCH model is applied by adding the expected and unexpected elements of trading activities (trading volume and open interest) as the independent variables. The results show that there is a negative contemporaneous relationship between the expected volume and volatility, but that a positive relationship exists between unexpected volume and volatility. On the contrary, the expected and unexpected open interest mitigate the volatility. Therefore, both trading volume and open interest should be considered together when information flows into the market.

An Empirical Analysis of the Price Volatility Characteristics of China’s Soybean Futures Market Based on ARIMA-GJR-GARCH Model

As the main force in the futures market, agricultural product futures occupy an important position in the China’s market. Taking the representative soybean futures in Dalian Commodity Futures Market of China as the research object, the relationship between price fluctuation characteristics and trading volume and open position was studied. The empirical results show that the price volatility of China’s soybean futures market has a “leverage effect.” The trading volume and open interest are divided into expected parts and unexpected parts, which are added to the conditional variance equation. The expected trading volume coefficient is estimated. Also, the estimated value of the expected open interest coefficient is, respectively, smaller than the estimated value of the unexpected trading volume coefficient and the estimated value of the unexpected open interest coefficient. Therefore, the impact of expected trading volume on the price fluctuation of China’s soybean futures market is less than that of unexpected trading volume on the price of soybean futures market. This paper adds transaction volume as an information flow to the variance of the conditional equation innovatively and also observes transaction volume as the relationship between conditional variance and price fluctuations.

Analysis of Volatility Volume and Open Interest for Nifty Index Futures Using GARCH Analysis and VAR Model

The generalized autoregressive conditional heteroscedastic model (GARCH) is used to estimate volatility for Nifty Index futures on day trades. The purpose is to find out if a contemporaneous or causal relation exists between volatility volume and open interest for Nifty Index futures traded on the National Stock Exchange of India, and the extent and direction of these relationships. A complete absence of bidirectional causality in any particular instance depicts noise trading and empirical analysis according to this study establishes that volume has a stronger impact on volatility compared to open interest. Furthermore, the impulse originating from volatility of volume and open interest is low.

On the price volatility of steel futures and its influencing factors in China

Steel futures have the function of price discovery and hedging. Steel related enterprises can judge the hedging strategy through the direction of steel futures price volatility, and reasonably avoid the risk brought by price volatility. Therefore, it is particularly important to study steel futures price volatility and its influencing factors. Because steel futures in China have characteristics of peak and rear tail aggregation, the paper constructs Model of GARCH (1,1) to make positive analysis of futures price volatility and its influencing factors of deformed steel bars and hot rolled coils, and the following conclusions have been drawn: (1) The volume and open interest of deformed steel bars have very significant explanatory ability to futures price volatility of deformed steel bars; (2) The volume and open interest of hot rolled coils also have very significant explanatory ability to futures price volatility of hot rolled coils; (3) The sustainable capacity of the price volatility of deformed steel bars and hot rolled coils is relatively small; (4) Iron ore price have no obvious explanatory ability to futures price volatility. Finally, some managerial implications and suggestions are derived from the analysis of the proposed model.

Modeling and Forecasting Gold Prices

The aim of this paper is to explore the reasons of gold price volatility. It analyses the information function of the gold future market by open interest contracts as speculation effect, and further fundamental factors including inflation, Chinese yuan per dollar, Japanese yen per dollar, dollar per euro, interest rate, oil price, and stock price, in the short-run. The study proceeds to build a Dynamic OLS model for long-run equilibrium to produce reliable gold price forecasts using the following variables: gold demand, gold supply, inflation, USD/SDR exchange rate, speculation, interest rate, oil price, and stock prices. Findings prove that in the short-run, changes in gold price does granger cause changes in open interest, and changes in Japanese yen per dollar does granger cause changes in gold price. However, in the long-run, the results prove that gold demand, gold supply, USD/SDR exchange rate, inflation, speculation, interest rate, and oil price are associated in a long-run relationship.

Modeling and Forecasting Gold Prices

The aim of this paper is to explore the reasons of gold price volatility. It analyses the information function of the gold future market by open interest contracts as speculation effect, and further fundamental factors including inflation, Chinese yuan per dollar, Japanese yen per dollar, dollar per euro, interest rate, oil price, and stock price, in the short-run. The study proceeds to build a Dynamic OLS model for long-run equilibrium to produce reliable gold price forecasts using the following variables: gold demand, gold supply, inflation, USD/SDR exchange rate, speculation, interest rate, oil price, and stock prices. Findings prove that in the short-run, changes in gold price does granger cause changes in open interest, and changes in Japanese yen per dollar does granger cause changes in gold price. However, in the long-run, the results prove that gold demand, gold supply, USD/SDR exchange rate, inflation, speculation, interest rate, and oil price are associated in a long-run relationship.References

Modeling and Forecasting Gold Prices

The aim of this paper is to explore the reasons of gold price volatility. It analyses the information function of the gold future market by open interest contracts as speculation effect, and further fundamental factors including inflation, Chinese yuan per dollar, Japanese yen per dollar, dollar per euro, interest rate, oil price, and stock price, in the short-run. The study proceeds to build a Dynamic OLS model for long-run equilibrium to produce reliable gold price forecasts using the following variables: gold demand, gold supply, inflation, USD/SDR exchange rate, speculation, interest rate, oil price, and stock prices. Findings prove that in the short-run, changes in gold price does granger cause changes in open interest, and changes in Japanese yen per dollar does granger cause changes in gold price. However, in the long-run, the results prove that gold demand, gold supply, USD/SDR exchange rate, inflation, speculation, interest rate, and oil price are associated in a long-run relationship.