A Hybrid ARCH-M and BP Neural Network Model For GSCI Futures Price Forecasting

Authors:
Wen Bo;Wang Shouyang;K. K. Lai
Affiliations:
Institute of System Science, Academy of Mathematics and System Sciences Chinese Academy of Sciences, BeiJing, China;Institute of System Science, Academy of Mathematics and System Sciences Chinese Academy of Sciences, BeiJing, China;Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong
Venue:
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Year:
2007

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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), CGARCH(1,1), EGARCH(1,1) and ARIMA-ANN models on the RMSE, MAPE, Theil IC evaluation criteria.