A hybrid ARCH-M and BP neural network model for GSCI futures price forecasting

Abstract

As a versatile investment tool in energy markets for speculators and hedgers, the Goldman Sachs Commodity Index (GSCI) futures are quite well known. Therefore, this paper proposes a hybrid model incorporating ARCH family models and ANN model to forecast GSCI futures price. Empirical results show that the hybrid ARCH(1)-M-ANN model is superior to ARIMA, ARCH(1), GARCH(1,1), EGARCH(1,1) and ARIMA-ANN models on the RMSE, MAPE, Theil IC evaluation criteria. © Springer-Verlag Berlin Heidelberg 2007.