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The Performance of Hybrid ARIMA-GARCH Modeling and Forecasting Oil Price

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  • Chaido Dritsaki

    (Department of Accounting and Finance, School of Management and Economics, Western Macedonia University of Applied Sciences, Greece.)

Abstract

Modeling and forecasting oil prices is an important issue for many researchers. One of the methods used in forecasting oil prices is Box-Jenkins methodology through ARIMA models. Although these models provide accurate forecasting over a short time period, they are not able to handle the volatility and nonlinearity presented on data series. For this reason, on this paper we examine a hybrid ARIMA-GARCH model in order to forecast the volatility in the return of oil prices. Moreover, on this paper, the Box-Cox transformation is used for data smoothing for the stabilization of variance and reduction of heteroscedasticity. Parameters estimation in the hybrid ARIMA-GARCH model is employed by ML (Maximum Likelihood) method using the steps of Marquardt s algorithm (1963) and Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm for optimization. The results of the paper showed that the hybridation of ARIMA(33,0,14)-GARCH(1,2) model following normal distribution is the most suitable for forecasting the returns of oil prices. Finally, we use both the dynamic and static procedure for forecasting. The results showed that the static procedure provides with better forecasting than the dynamic.

Suggested Citation

  • Chaido Dritsaki, 2018. "The Performance of Hybrid ARIMA-GARCH Modeling and Forecasting Oil Price," International Journal of Energy Economics and Policy, Econjournals, vol. 8(3), pages 14-21.
    • Handle: RePEc:eco:journ2:2018-03-3
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    Cited by:

    1. Melina Dritsaki & Chaido Dritsaki, 2020. "Forecasting European Union CO2 Emissions Using Autoregressive Integrated Moving Average-autoregressive Conditional Heteroscedasticity Models," International Journal of Energy Economics and Policy, Econjournals, vol. 10(4), pages 411-423.
    2. Ramesh Bollapragada & Akash Mankude & V. Udayabhanu, 2021. "Forecasting the price of crude oil," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 48(2), pages 207-231, June.
    3. Pablo Cansado-Bravo & Carlos Rodríguez-Monroy, 2018. "Persistence of Oil Prices in Gas Import Prices and the Resilience of the Oil-Indexation Mechanism. The Case of Spanish Gas Import Prices," Energies, MDPI, vol. 11(12), pages 1-17, December.

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    More about this item

    Keywords

    ARIMA; GARCH; oil price forecasting; hybrid ARIMA-GARCH; Box-Cox transformation;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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