A COMPARISON OF VAR AND NEURAL NETWORKS WITH GENETIC ALGORITHM IN FORECASTING PRICE OF OIL

Sam Mirmirani and Hsi Cheng Li

A chapter in Applications of Artificial Intelligence in Finance and Economics, 2004, pp 203-223 from Emerald Group Publishing Limited

Abstract: This study applies VAR and ANN techniques to make ex-post forecast of U.S. oil price movements. The VAR-based forecast uses three endogenous variables: lagged oil price, lagged oil supply and lagged energy consumption. However, the VAR model suggests that the impacts of oil supply and energy consumption has limited impacts on oil price movement. The forecast of the genetic algorithm-based ANN model is made by using oil supply, energy consumption, and money supply (M1). Root mean squared error and mean absolute error have been used as the evaluation criteria. Our analysis suggests that the BPN-GA model noticeably outperforms the VAR model.

Date: 2004
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.emerald.com/insight/content/doi/10.101 ... d&utm_campaign=repec (text/html)
https://www.emerald.com/insight/content/doi/10.101 ... d&utm_campaign=repec (application/pdf)
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-9053(04)19008-7

DOI: 10.1016/S0731-9053(04)19008-7

Access Statistics for this chapter

More chapters in Advances in Econometrics from Emerald Group Publishing Limited
Bibliographic data for series maintained by Emerald Support ().