A VAR-SVM model for crude oil price forecasting

Lu-Tao Zhao, Lei Cheng, Yongtao Wan, Hao Zhang and Zhigang Zhang

International Journal of Global Energy Issues, 2015, vol. 38, issue 1/2/3, 126-144

Abstract: In recent years, the complexity and variability of international crude oil price have had an increasingly greater impact on society's economic development. Therefore, the accurate forecasting of crude oil price is helpful to maintain economic stability and avoid risks. This paper analyses the influencing factors, including market factors and non-market factors and then uses the Vector Autoregression (VAR) model to measure the relationship between oil price and those factors. Based on the results of VAR model, we put forward a new model - VAR-SVM, which is based on VAR and Support Vector Machine (SVM). Using VAR-SVM, we can make more accurate prediction of crude oil prices. Genetic Algorithm (GA) is employed to select model parameters. From the empirical results we can see that VAR-SVM model is superiority in accuracy and effectiveness comparing with the other forecasting models, such as VAR model, CGARCH model, Artificial Neural Network (ANN) model and autoregression SVM model.

Keywords: crude oil prices; price forecasting; VAR-SVM model; GAs; genetic algorithms; vector autoregression; VAR; support vector machines; SVM; modelling. (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (12)

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