Forecasting oil prices
Stavros Degiannakis and
MPRA Paper from University Library of Munich, Germany
Accurate and economically useful oil price forecasts have gained significant importance over the last decade. The majority of the studies use information from the oil market fundamentals to generate oil price forecasts. Nevertheless, the extant literature has convincingly shown that oil prices are nowadays interconnected with the financial and commodities markets. Despite this, there is scarce evidence as to whether information from these markets could improve the forecasting accuracy of oil prices. Even more, there is limited knowledge whether high frequency data, given their rich information, could improve monthly oil prices. In this study we fill this void, employing a Mixed Data-Sampling (MIDAS) method using both oil market fundamentals and high frequency data from 15 financial and commodities assets. Our findings show that either the daily realized volatilities or daily returns of these assets significantly improve oil price forecasts relatively to the no-change forecast, as well as, relatively to the well-established models of the literature. These results hold true even when we consider tranquil and turbulent oil market conditions.
Keywords: Oil price forecasting; Brent crude oil; intra-day data; MIDAS. (search for similar items in EconPapers)
JEL-codes: C53 G14 G15 Q43 Q47 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ene and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4) Track citations by RSS feed
Downloads: (external link)
https://mpra.ub.uni-muenchen.de/77531/1/MPRA_paper_77531.pdf original version (application/pdf)
https://mpra.ub.uni-muenchen.de/79387/8/MPRA_paper_79387.pdf revised version (application/pdf)
https://mpra.ub.uni-muenchen.de/86441/16/MPRA_paper_86441.pdf revised version (application/pdf)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:77531
Access Statistics for this paper
More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().