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Oil Price Factors: Forecasting on the Base of Modified Auto-regressive Integrated Moving Average Model

Author

Listed:
  • Anthony Nyangarika

    (School of Management and Economics, Beijing Institute of Technology, Beijing, China,)

  • Alexey Mikhaylov

    (Department of Financial Markets and Banks, Financial University under the Government of the Russian Federation, Moscow, Russia,)

  • Ulf Henning Richter

    (School of Economics and Management, Tongji University, Tongji, China.)

Abstract

The paper proposes modification of auto-regressive integrated moving average model for finding the parameters of estimation and forecasts using exponential smoothing. The study use data Brent crude oil price and gas prices in the period from January 1991 to December 2016. The result of the study showed an improvement in the accuracy of the predicted values, while the emissions occurred near the end of the time series. It has minimal or no effect on other emissions of this data series. The study suggests that investors can predict prices analyzing the possible risks in oil futures markets.

Suggested Citation

  • Anthony Nyangarika & Alexey Mikhaylov & Ulf Henning Richter, 2019. "Oil Price Factors: Forecasting on the Base of Modified Auto-regressive Integrated Moving Average Model," International Journal of Energy Economics and Policy, Econjournals, vol. 9(1), pages 149-159.
    • Handle: RePEc:eco:journ2:2019-01-19
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    References listed on IDEAS

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    6. Shaher Al-Gounmeein Remal & Ismail Mohd Tahir, 2021. "Modelling and forecasting monthly Brent crude oil prices: a long memory and volatility approach," Statistics in Transition New Series, Statistics Poland, vol. 22(1), pages 29-54, March.
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    16. Piersanti, Giovanni & Piersanti, Mirko & Cicone, Antonio & Canofari, Paolo & Di Domizio, Marco, 2020. "An inquiry into the structure and dynamics of crude oil price using the fast iterative filtering algorithm," Energy Economics, Elsevier, vol. 92(C).
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    21. Valeriia Denisova, 2019. "Energy Efficiency as a Way to Ecological Safety: Evidence From Russia," International Journal of Energy Economics and Policy, Econjournals, vol. 9(5), pages 32-37.
    22. Jaehyung An & Alexey Mikhaylov & Nikita Moiseev, 2019. "Oil Price Predictors: Machine Learning Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 9(5), pages 1-6.
    23. Thomas Burkhardt & Diana Stepanova & Leonid Ratkin & Ismail Ismailov & Oleg Lavrushin & Natalia Sokolinskaya & Mir Sayed Shah Danish & Tomonobu Senjyu & Serhat Yuksel & Hasan Dincer, 2021. "Introduction of Biofuels as a Way of Solving Ecological Problems," International Journal of Energy Economics and Policy, Econjournals, vol. 11(2), pages 187-193.
    24. Jaehyung An & Alexey Mikhaylov & Sang-Uk Jung, 2020. "The Strategy of South Korea in the Global Oil Market," Energies, MDPI, vol. 13(10), pages 1-8, May.

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    More about this item

    Keywords

    Auto-regressive Integrated Moving Average Model; Econometric Model; Oil Price Forecast;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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