Dynamic wavelet neural network model for forecasting returns of SHFE copper futures price

Abstract

Appropriate forecasting of commodity futures price returns is of crucial importance to achieve hedging effectiveness against the returns volatility risk. This paper presents a nonparametric dynamic recurrent wavelet neural network model for forecasting returns of Shanghai Futures Exchange (SHFE) copper futures price. The proposed model employs a wavelet basis function as the activation function for hidden-layer neurons of the neural network. The aim of this arrangement is to incorporate the fractal properties discovered in futures price return series. In the wavelet transform domain, fractal self-similarity information of the returns series over a certain time scale can be extracted. Input variables are analyzed and selected to facilitate effective forecasting. Statistical indices such as normal mean square error (NMSE) are adopted to evaluate forecasting performance of the proposed model. The forecasted result shows that dynamic wavelet neural network has good prediction properties compared with traditional linear statistical model such as ARIMA and other neural network forecasting models.