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Prediction of Oil Price using ARMA Method for Years 2003 to 2011

Author

Listed:
  • Abdalali Monsef

    (Payame Noor University)

  • Amir Hortmani

    (Islamic Azad University)

  • Khadijeh Hamzeh

    (Payame Noor University)

Abstract

One main purpose of economic analysis is prediction of economic variables. For this reason, various methods have been developed in this context. One important challenge in prediction of time series is precise prediction without any computational complexities. It is usually assumed that autoregressive moving-average (ARMA) models have this ability with a high accuracy. In this study, ARMA method has been used to predict time series of oil price. To determine the order of this process Akaike and Schwartz’s Bayesian criteria have been used. The results show that the best answers are obtained by ARMA (1,0) model for static predictions and ARMA (3,2) model for dynamic predictions.

Suggested Citation

  • Abdalali Monsef & Amir Hortmani & Khadijeh Hamzeh, 2013. "Prediction of Oil Price using ARMA Method for Years 2003 to 2011," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 3(1), pages 271-279, January.
    • Handle: RePEc:hur:ijaraf:v:3:y:2013:i:1:p:271-279
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    References listed on IDEAS

    as
    1. Buchanan, W. K. & Hodges, P. & Theis, J., 2001. "Which way the natural gas price: an attempt to predict the direction of natural gas spot price movements using trader positions," Energy Economics, Elsevier, vol. 23(3), pages 279-293, May.
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    Cited by:

    1. Asit Kumar Das & Debahuti Mishra & Kaberi Das & Pradeep Kumar Mallick & Sachin Kumar & Mikhail Zymbler & Hesham El-Sayed, 2022. "Prophesying the Short-Term Dynamics of the Crude Oil Future Price by Adopting the Survival of the Fittest Principle of Improved Grey Optimization and Extreme Learning Machine," Mathematics, MDPI, vol. 10(7), pages 1-33, March.

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