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Forecasting oil prices

Author

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  • Degiannakis, Stavros
  • Filis, George

Abstract

Accurate and economically useful oil price forecasts have gained significant importance over the last decade. The majority of the studies use information from the oil market fundamentals to generate oil price forecasts. Nevertheless, the extant literature has convincingly shown that oil prices are nowadays interconnected with the financial and commodities markets. Despite this, there is scarce evidence as to whether information from these markets could improve the forecasting accuracy of oil prices. Even more, there is limited knowledge whether high frequency data, given their rich information, could improve monthly oil prices. In this study we fill this void, employing a Mixed Data-Sampling (MIDAS) method using both oil market fundamentals and high frequency data from 15 financial and commodities assets. Our findings show that either the daily realized volatilities or daily returns of these assets significantly improve oil price forecasts relatively to the no-change forecast, as well as, relatively to the well-established models of the literature. These results hold true even when we consider tranquil and turbulent oil market conditions.

Suggested Citation

  • Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil prices," MPRA Paper 77531, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:77531
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    References listed on IDEAS

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    Cited by:

    1. Riza Demirer & Rangan Gupta & Qiang Ji & Aviral Kumar Tiwari, 2018. "Geopolitical Risks and the Predictability of Regional Oil Returns and Volatility," Working Papers 201860, University of Pretoria, Department of Economics.
    2. Rangan Gupta & Patrick Kanda & Aviral Kumar Tiwari & Mark E. Wohar, 2018. "Time-Varying Predictability of Oil Market Movements Over a Century of Data: The Role of US Financial Stress," Working Papers 201848, University of Pretoria, Department of Economics.

    More about this item

    Keywords

    Oil price forecasting; Brent crude oil; intra-day data; MIDAS.;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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