Do high-frequency financial data help forecast oil prices? The MIDAS touch at work
AbstractThe substantial variation in the real price of oil since 2003 has renewed interest in the question of how to forecast monthly and quarterly oil prices. There also has been increased interest in the link between financial markets and oil markets, including the question of whether financial market information helps forecast the real price of oil in physical markets. An obvious advantage of financial data in forecasting oil prices is their availability in real time on a daily or weekly basis. We investigate whether mixed-frequency models may be used to take advantage of these rich data sets. We show that, among a range of alternative high-frequency predictors, especially changes in U.S. crude oil inventories produce substantial and statistically significant real-time improvements in forecast accuracy. The preferred MIDAS model reduces the MSPE by as much as 16 percent compared with the no-change forecast and has statistically significant directional accuracy as high as 82 percent. This MIDAS forecast also is more accurate than a mixed-frequency realtime VAR forecast, but not systematically more accurate than the corresponding forecast based on monthly inventories. We conclude that typically not much is lost by ignoring high-frequency financial data in forecasting the monthly real price of oil. --
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Bibliographic InfoPaper provided by Center for Financial Studies (CFS) in its series CFS Working Paper Series with number 2013/22.
Date of creation: 2013
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Mixed frequency; Real-time data; Oil price; Forecasts;
Other versions of this item:
- Christiane Baumeister & Pierre Guérin & Lutz Kilian, 2014. "Do High-Frequency Financial Data Help Forecast Oil Prices? The MIDAS Touch at Work," Working Papers 14-11, Bank of Canada.
- Baumeister, Christiane & Guérin, Pierre & Kilian, Lutz, 2013. "Do High-Frequency Financial Data Help Forecast Oil Prices? The MIDAS Touch at Work," CEPR Discussion Papers 9768, C.E.P.R. Discussion Papers.
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
- Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-11-29 (All new papers)
- NEP-ENE-2013-11-29 (Energy Economics)
- NEP-FOR-2013-11-29 (Forecasting)
- NEP-MST-2013-11-29 (Market Microstructure)
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