FEEM working papers "Note di lavoro" series
2011 .091

Oil Price Forecast Evaluation with Flexible Loss Functions


Autori: Andrea Bastianin, Matteo Manera, Anil Markandya, Elisa Scarpa
Serie: Energy: Resources and Markets
Editor: Giuseppe Sammarco
Tipo: Book
Parole chiave: Oil Price, WTI Spot and Futures Prices, Forecasting, Econometric Models
Numero JEL: C52, C53, Q32, Q43
JEL: Forthcoming as a chapter of the book Energy Economics, eds. S. Ramos and H. Vega
Pubblicato in: Springer
Data: 2013

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.

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