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Forecasting oil prices: High-frequency financial data are indeed useful

Stavros Degiannakis () and George Filis ()

Energy Economics, 2018, vol. 76, issue C, 388-402

Abstract: The paper examines the importance of combining high frequency financial information, along with the oil market fundamentals, in order to gain incremental forecasting accuracy for oil prices. Inspired by French et al. (1987) 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-run forecasting horizons, the combination of the latter with high-frequency financial data significantly improve oil price forecasts, by reducing the RMSE of the no-change forecast by approximately 68%. Results are even more impressive during the oil price collapse period of 2014–15. These findings suggest that we cannot ignore the information extracted from the financial markets when forecasting oil prices. Our results are both statistically and economically significant, as suggested by several robustness tests.

Keywords: Oil price forecasting; Brent crude oil; Intra-day data; MIDAS; EIA forecasts (search for similar items in EconPapers)
JEL-codes: C53 G14 G15 Q43 Q47 (search for similar items in EconPapers)
Date: 2018
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Energy Economics is currently edited by R. S. J. Tol, Beng Ang, Lance Bachmeier, Perry Sadorsky, Ugur Soytas and J. P. Weyant

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