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Oil Price Forecast Evaluation with Flexible Loss Functions

Author

Listed:
  • Andrea Bastianin

    (Department of Statistics, University of Milan-Bicocca and Fondazione Eni Enrico Mattei)

  • Matteo Manera

    (Department of Statistics, University of Milan-Bicocca and Fondazione Eni Enrico Mattei)

  • Anil Markandya

    (BC3 Basque Centre for Climate Change)

  • Elisa Scarpa

    (Edison Trading)

Abstract

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 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 suggest that, irrespective of the shape of the loss function, the class of financial models is to be preferred to time series models. Both financial and time series models are better than mixed and structural models. Results of the Diebold and Mariano test are not conclusive, for the loss differential seems to be statistically insignificant in the large majority of cases. Although the random walk model is not statistically outperformed by any of the alternative models, the empirical findings seem to suggest that theoretically well-grounded financial models are valid instruments for producing accurate forecasts of the WTI spot price.

Suggested Citation

  • Andrea Bastianin & Matteo Manera & Anil Markandya & Elisa Scarpa, 2011. "Oil Price Forecast Evaluation with Flexible Loss Functions," Working Papers 2011.91, Fondazione Eni Enrico Mattei.
  • Handle: RePEc:fem:femwpa:2011.91
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    References listed on IDEAS

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    Cited by:

    1. repec:eee:eneeco:v:65:y:2017:i:c:p:219-227 is not listed on IDEAS
    2. Janda, Karel & Krska, Stepan & Prusa, Jan, 2014. "Odhad nákladů na podporu české fotovoltaické energie
      [The Estimation of the Cost of Promotion of the Czech Photovoltaic Energy]
      ," MPRA Paper 54108, University Library of Munich, Germany.

    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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