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Evaluating the Empirical Performance of Alternative Econometric Models for Oil Price Forecasting

Author

Listed:
  • Matteo Manera

    (University of Milan Bicocca)

  • Chiara Longo

    (Fondazione Eni Enrico Mattei)

  • Anil Markandya

    (University of Bath and Fondazione Eni Enrico Mattei)

  • Elisa Scarpa

    (Risk Management Department, Intesa-San Paolo)

Abstract

The relevance of oil in the world economy explains why considerable effort has been devoted to the development of different types of econometric models for oil price forecasting. Several specifications have been proposed in the economic literature. Some are based on financial theory and concentrate on the relationship between spot and futures prices (“financial” models). Others assign a key role to variables explaining the characteristics of the physical oil market (“structural” models). The empirical literature is very far from any consensus about the appropriate model for oil price forecasting that should be implemented. Relative to the previous literature, this paper is novel in several respects. First of all, we test and systematically evaluate the ability of several alternative econometric specifications proposed in the literature to capture the dynamics of oil prices. Second, we analyse the effects of different data frequencies on the coefficient estimates and forecasts obtained using each selected econometric specification. Third, we compare different models at different data frequencies on a common sample and common data. Fourth, we evaluate the forecasting performance of each selected model using static and dynamic forecasts, as well as different measures of forecast errors. Finally, we propose a new class of models which combine the relevant aspects of the financial and structural specifications proposed in the literature (“mixed” models). Our empirical findings can be summarized as follows. Financial models in levels do not produce satisfactory forecasts for the WTI spot price. The financial error correction model yields accurate in-sample forecasts. Real and strategic variables alone are insufficient to capture the oil spot price dynamics in the forecasting sample. Our proposed mixed models are statistically adequate and exhibit accurate forecasts. Different data frequencies seem to affect the forecasting ability of the models under analysis.

Suggested Citation

  • Matteo Manera & Chiara Longo & Anil Markandya & Elisa Scarpa, 2007. "Evaluating the Empirical Performance of Alternative Econometric Models for Oil Price Forecasting," Working Papers 2007.4, Fondazione Eni Enrico Mattei.
  • Handle: RePEc:fem:femwpa:2007.4
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Zhang, Xun & Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2009. "Estimating the impact of extreme events on crude oil price: An EMD-based event analysis method," Energy Economics, Elsevier, vol. 31(5), pages 768-778, September.
    2. Jammazi, Rania, 2012. "Oil shock transmission to stock market returns: Wavelet-multivariate Markov switching GARCH approach," Energy, Elsevier, vol. 37(1), pages 430-454.
    3. Sanders, Dwight R. & Manfredo, Mark R. & Boris, Keith, 2009. "Evaluating information in multiple horizon forecasts: The DOE's energy price forecasts," Energy Economics, Elsevier, vol. 31(2), pages 189-196.
    4. Slabá, Monika & Gapko, Petr & Klimešová, Andrea, 2013. "Main drivers of natural gas prices in the Czech Republic after the market liberalisation," Energy Policy, Elsevier, vol. 52(C), pages 199-212.
    5. Ai Han & Yanan He & Yongmiao Hong & Shouyang Wang, 2013. "Forecasting Interval-valued Crude Oil Prices via Autoregressive Conditional Interval Models," WISE Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    6. Mamatzakis, E. & Koutsomanoli-Filippaki, A., 2014. "Testing the rationality of DOE's energy price forecasts under asymmetric loss preferences," Energy Policy, Elsevier, vol. 68(C), pages 567-575.
    7. Claudio Dicembrino & Pasquale Lucio Scandizzo, 2012. "The Fundamental and Speculative Components of the Oil Spot Price: A Real Option Value Approach," CEIS Research Paper 229, Tor Vergata University, CEIS, revised 18 Apr 2012.
    8. Ekins, Paul & Pollitt, Hector & Barton, Jennifer & Blobel, Daniel, 2011. "The implications for households of environmental tax reform (ETR) in Europe," Ecological Economics, Elsevier, vol. 70(12), pages 2472-2485.

    More about this item

    Keywords

    Oil Price; WTI Spot And Futures Prices; Forecasting; Econometric Models;

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q32 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - Exhaustible Resources and Economic Development
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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