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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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    1. Asger Lunde & Peter R. Hansen, 2005. "A forecast comparison of volatility models: does anything beat a GARCH(1,1)?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889.
    2. Whitney K. Newey & Kenneth D. West, 1994. "Automatic Lag Selection in Covariance Matrix Estimation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 61(4), pages 631-653.
    3. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    4. Mohammadi, Hassan, 2009. "Electricity prices and fuel costs: Long-run relations and short-run dynamics," Energy Economics, Elsevier, vol. 31(3), pages 503-509, May.
    5. Elder, John & Serletis, Apostolos, 2009. "Oil price uncertainty in Canada," Energy Economics, Elsevier, vol. 31(6), pages 852-856, November.
    6. Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2008. "Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm," Energy Economics, Elsevier, vol. 30(5), pages 2623-2635, September.
    7. Agnolucci, Paolo, 2009. "Volatility in crude oil futures: A comparison of the predictive ability of GARCH and implied volatility models," Energy Economics, Elsevier, vol. 31(2), pages 316-321, March.
    8. Rotemberg, Julio J & Woodford, Michael, 1996. "Imperfect Competition and the Effects of Energy Price Increases on Economic Activity," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 28(4), pages 550-577, November.
    9. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    10. Hamilton, James D & Herrera, Ana Maria, 2004. "Oil Shocks and Aggregate Macroeconomic Behavior: The Role of Monetary Policy: Comment," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 36(2), pages 265-286, April.
    11. Yang, C. W. & Hwang, M. J. & Huang, B. N., 2002. "An analysis of factors affecting price volatility of the US oil market," Energy Economics, Elsevier, vol. 24(2), pages 107-119, March.
    12. Sadorsky, Perry, 2006. "Modeling and forecasting petroleum futures volatility," Energy Economics, Elsevier, vol. 28(4), pages 467-488, July.
    13. Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
    14. MacKinnon, James G, 1996. "Numerical Distribution Functions for Unit Root and Cointegration Tests," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(6), pages 601-618, Nov.-Dec..
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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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