Probing Trading Activities in Commodity Futures Market via Volatility Modeling

Abstract

In the context of the great fluctuation of the global financial market, it is particularly important to forecast the changing futures market. Inspired by the utilization of the Heterogeneous Autoregressive model of the Realized Volatility (HAR-RV) model in price volatility forecasting, the parameters in this model could be a fair indicator of trading activities in the market. To test this hypothesis, this HAR-RV model is applied to analyze some representative commodities futures in China. The HAR-RV model is created to forecast the future volatility patterns using existed fluctuations. We used the least square method to do the regression analysis and used the volatility of a day, a week, and a month as the variables to get the degree of influence of each variable on the future volatility, namely. Thus, we can tell whether the price of a futures commodity is more affected by short-term fluctuations or long-term fluctuations by comparing different. Preliminary results show different patterns between iron ore and soybean meal futures models, pinpointing the trading frequencies in the corresponding markets. The price of iron ore futures is largely influenced by weekly volatility, while the price of soybean meal futures is affected significantly by monthly fluctuation.