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Forecasting oil prices

Degiannakis, Stavros and Filis, George (2018): Forecasting oil prices.

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The paper examines the importance of combining high frequency information, along with the market fundamentals, in order to gain incremental forecasting accuracy for oil prices. Inspired by French et al. (1986) and Bollerslev et al. (1988), who maintain that future asset returns are also influenced by past volatility, we use daily volatilities and returns from financial and commodity markets to generate real out-of-sample forecasts for the monthly oil futures prices. Our results convincingly show that although the oil market fundamentals are useful for long term forecasting horizons, the combination of the latter with asset realized volatilities, as these are constructed using ultra-high frequency data, significantly improve oil price forecasts in short-run horizons. These findings are both statistically and economically significant, as suggested by several robustness tests.

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