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Application of Markov Model in Crude Oil Price Forecasting

Author

Listed:
  • Nuhu Isah

    (Universiti Tun Hussein Onn Malaysia)

  • Abdul Talib Bon

    (Universiti Tun Hussein Onn Malaysia)

Abstract

Crude oil is an important energy commodity to mankind. Several causes have made crude oil prices to be volatile. The fluctuation of crude oil prices has affected many related sectors and stock market indices. Hence, forecasting the crude oil prices is essential to avoid the future prices of the non-renewable natural resources to rise. In this study, daily crude oil prices data was obtained from WTI dated 2 January to 29 May 2015. We used Markov Model (MM) approach in forecasting the crude oil prices. In this study, the analyses were done using EViews and Maple software where the potential of this software in forecasting daily crude oil prices time series data was explored. Based on the study, we concluded that MM model is able to produce accurate forecast based on a description of history patterns in crude oil prices.

Suggested Citation

  • Nuhu Isah & Abdul Talib Bon, 2017. "Application of Markov Model in Crude Oil Price Forecasting," Traektoriâ Nauki = Path of Science, Altezoro, s.r.o. & Dialog, vol. 3(8(25)), pages 1007-1012, August.
    • Handle: RePEc:pos:journl:25-2
    DOI: 10.22178/pos.25-3
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    References listed on IDEAS

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    1. Kaufmann, Robert K. & Bradford, Andrew & Belanger, Laura H. & Mclaughlin, John P. & Miki, Yosuke, 2008. "Determinants of OPEC production: Implications for OPEC behavior," Energy Economics, Elsevier, vol. 30(2), pages 333-351, March.
    2. Kaufmann, Robert K., 2011. "The role of market fundamentals and speculation in recent price changes for crude oil," Energy Policy, Elsevier, vol. 39(1), pages 105-115, January.
    3. Radchenko, Stanislav, 2005. "Oil price volatility and the asymmetric response of gasoline prices to oil price increases and decreases," Energy Economics, Elsevier, vol. 27(5), pages 708-730, September.
    4. Xiu, Shuangning & Shahbazi, Abolghasem, 2012. "Bio-oil production and upgrading research: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4406-4414.
    5. Tang, Linghui & Hammoudeh, Shawkat, 2002. "An empirical exploration of the world oil price under the target zone model," Energy Economics, Elsevier, vol. 24(6), pages 577-596, November.
    6. Lutz Kilian & Daniel P. Murphy, 2014. "The Role Of Inventories And Speculative Trading In The Global Market For Crude Oil," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(3), pages 454-478, April.
    7. Bopp, Anthony E. & Lady, George M., 1991. "A comparison of petroleum futures versus spot prices as predictors of prices in the future," Energy Economics, Elsevier, vol. 13(4), pages 274-282, October.
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    Cited by:

    1. Li, Guohui & Yin, Shibo & Yang, Hong, 2022. "A novel crude oil prices forecasting model based on secondary decomposition," Energy, Elsevier, vol. 257(C).

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    More about this item

    Keywords

    forecasting; crude oil; price; Markov model.;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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