G2TT
来源类型Discussion paper
规范类型论文
来源IDDP9768
DP9768 Do High-Frequency Financial Data Help Forecast Oil Prices? The MIDAS Touch at Work
Lutz Kilian; Christiane Baumeister
发表日期2013-12-01
出版年2013
语种英语
摘要The 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 real-time 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.
主题International Macroeconomics
关键词forecasts Mixed frequency Oil price Real-time data
URLhttps://cepr.org/publications/dp9768
来源智库Centre for Economic Policy Research (United Kingdom)
资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/538604
推荐引用方式
GB/T 7714
Lutz Kilian,Christiane Baumeister. DP9768 Do High-Frequency Financial Data Help Forecast Oil Prices? The MIDAS Touch at Work. 2013.
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