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Title:Oil price forecast evaluation with flexible loss functions PDF Logo
Authors:Bastianin, Andrea
Manera, Matteo
Markandya, Anil
Scarpa, Elisa
Issue Date:2011
Series/Report no.:Nota di lavoro // Fondazione Eni Enrico Mattei: Energy: Resources and Markets 91.2011
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.
Subjects:Oil Price
WTI Spot and Futures Prices
Forecasting
Econometric Models
JEL:C52
C53
Q32
Q43
Document Type:Working Paper
Appears in Collections:FEEM Working Papers, Fondazione Eni Enrico Mattei

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