Abstract
This article presents results from modelling spot oil prices by Dynamic Model Averaging (DMA). First, based on a literature review and availability of data, the following oil price drivers have been selected: stock prices indices, stock prices volatility index, exchange rates, global economic activity, interest rates, supply and demand indicators and inventories level. Next, they have been included as explanatory variables in various DMA models with different initial parameters. Monthly data between January 1986 and December 2015 has been analyzed. Several variations of DMA models have been constructed, because DMA requires the initial setting of certain parameters. Interestingly, DMA has occurred to be robust to setting different values to these parameters. It has also occurred that the quality of prediction is the highest for the model with the drivers solely connected with the stock markets behavior. Drivers connected with macroeconomic fundamental indicators have not been found so important. This observation can serve as an argument favoring the hypothesis of the increasing financialization of the oil market, at least in the short-term period. The predictions from other, slightly different modelling variations based on DMA methodology, have happened to be consistent with each other in general. Many constructed models have outperformed alternative forecasting methods. It has also been found that normalization of the initial data, although not necessary for DMA from the theoretical point of view, significantly improves the quality of prediction.
Highlights
ObjectivesAs the aim of this research is to find drivers of oil price, this serves as another argument in favor of normalizing data
Forecasting oil prices is an important problem in the energy market
Drivers included in “full” Dynamic Model Averaging (DMA) models are presented in Table 2, and drivers which have emerged to models3.have been estimated
Summary
As the aim of this research is to find drivers of oil price, this serves as another argument in favor of normalizing data
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