Abstract: In view of the complexity of soybean futures price fluctuation and the diversity of influencing factors, soybean futures price forecasting is conducted by introducing the dynamic model averaging theory. It should be noted that this technique is capable of dynamically choosing the explanatory variables and coefficient of variation, which will maximize the utilization of various information to control the models effectively and coefficients uncertainty, and finally improve the forecasting performance. More specifically, an analysis framework on influencing factors of soybean futures price is proposed. The time-vary characteristics of soybean futures price's influencing factors are identified from the perspective of futures markets and economics environment, and then a forecasting model for soybean futures price is constructed. Furthermore, covering an out-of-sample period from July 30, 2009 to June 15, 2017, the forecasting performance of the proposed forecasting model is evaluated and compared with six benchmarks on the basis of accuracy measures and Diebold-Mariano test. The experimental results show that the dynamic model averaging can effectively identify the influence degree of each explanatory variables on soybean futures price, and at the same time, outperform the Bayes model averaging, time-varying parameter model, and random walk in soybean futures price forecasting. Policymakers should be cognizant of the fact that there are many potential predictors that can help to forecast the Chinese soybean futures price, and the predictive powers of these predictors vary over time.

Key words: agricultural futures, forecasting model, dynamic model averaging, time-varying parameter model