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Oil prices -- Brownian motion or mean reversion? A study using a one year ahead density forecast criterion

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  • Meade, Nigel
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    Abstract

    For oil related investment appraisal, an accurate description of the evolving uncertainty in the oil price is essential. For example, when using real option theory to value an investment, a density function for the future price of oil is central to the option valuation. The literature on oil pricing offers two views. The arbitrage pricing theory literature for oil suggests geometric Brownian motion and mean reversion models. Empirically driven literature suggests ARMA-GARCH models. In addition to reflecting the volatility of the market, the density function of future prices should also incorporate the uncertainty due to price jumps, a common occurrence in the oil market. In this study, the accuracy of density forecasts for up to a year ahead is the major criterion for a comparison of a range of models of oil price behaviour, both those proposed in the literature and following from data analysis. The Kullbach Leibler information criterion is used to measure the accuracy of density forecasts. Using two crude oil price series, Brent and West Texas Intermediate (WTI) representing the US market, we demonstrate that accurate density forecasts are achievable for up to nearly two years ahead using a mixture of two Gaussians innovation processes with GARCH and no mean reversion.

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    Bibliographic Info

    Article provided by Elsevier in its journal Energy Economics.

    Volume (Year): 32 (2010)
    Issue (Month): 6 (November)
    Pages: 1485-1498

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    Handle: RePEc:eee:eneeco:v:32:y:2010:i:6:p:1485-1498

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    Web page: http://www.elsevier.com/locate/eneco

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    Keywords: Time series Density forecasting Commodity prices;

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    Cited by:
    1. Xu, Weijun & Sun, Qi & Xiao, Weilin, 2012. "A new energy model to capture the behavior of energy price processes," Economic Modelling, Elsevier, vol. 29(5), pages 1585-1591.
    2. Julio Alonso Cifuentes & Andrés Arcila Vásquez, 2012. "Un modelo de predicciones diarias para contratos de futuros de azúcar," REVISTA ECONOMÍA & REGIÓN, UNIVERSIDAD TECNOLÓGICA DE BOLÍVAR.
    3. Calili, Rodrigo F. & Souza, Reinaldo C. & Galli, Alain & Armstrong, Margaret & Marcato, André Luis M., 2014. "Estimating the cost savings and avoided CO2 emissions in Brazil by implementing energy efficient policies," Energy Policy, Elsevier, vol. 67(C), pages 4-15.
    4. Daniel Ziegler & Katrin Schmitz & Christoph Weber, 2012. "Optimal electricity generation portfolios," Computational Management Science, Springer, vol. 9(3), pages 381-399, August.
    5. Jin, Xiaoye & Xiaowen Lin, Sharon & Tamvakis, Michael, 2012. "Volatility transmission and volatility impulse response functions in crude oil markets," Energy Economics, Elsevier, vol. 34(6), pages 2125-2134.
    6. Sun, Qi & Xu, Weijun & Xiao, Weilin, 2013. "An empirical estimation for mean-reverting coal prices with long memory," Economic Modelling, Elsevier, vol. 33(C), pages 174-181.
    7. Kovacevic, Raimund M. & Paraschiv, Florentina, 2012. "Medium-term Planning for Thermal Electricity Production," Working Papers on Finance 1220, University of St. Gallen, School of Finance.

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