Godarzi, A.A. and Madadi Amiri, R. and Talaei, A. and Jamasb, T. (2014) 'Predicting oil price movements : a dynamic Artificial Neural Network approach.', Energy policy., 68 . pp. 371-382.
Price of oil is important for the economies of oil exporting and oil importing countries alike. Therefore, insight into the likely future behaviour and patterns of oil prices can improve economic planning and reduce the impacts of oil market fluctuations. This paper aims to improve the application of Artificial Neural Network (ANN) techniques to prediction of oil price. We develop a dynamic Nonlinear Auto Regressive model with eXogenous input (NARX) as a form of ANN to account for the time factor. We estimate the model using macroeconomic data from OECD countries. In order to compare the results, we develop time series and ANN static models. We then use the output of time series model to develop a NARX model. The NARX model is trained with historical data from 1974 to 2004 and the results are verified with data from 2005 to 2009. The results show that NARX model is more accurate than time series and static ANN models in predicting oil prices in general as well as in predicting the occurrence of oil price shocks.
|Keywords:||Oil price forecasting, Time series model, NARX model.|
|Full text:||(AM) Accepted Manuscript|
Download PDF (766Kb)
|Publisher Web site:||http://dx.doi.org/10.1016/j.enpol.2013.12.049|
|Publisher statement:||NOTICE: this is the author’s version of a work that was accepted for publication in Energy Policy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Energy Policy, 68, 2014, 10.1016/j.enpol.2013.12.049.|
|Record Created:||16 Jun 2014 11:50|
|Last Modified:||05 May 2015 17:37|
|Social bookmarking:||Export: EndNote, Zotero | BibTex|
|Look up in GoogleScholar | Find in a UK Library|