Do high-frequency financial data help forecast oil prices? : the MIDAS touch at work

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 and energy market data in forecasting oil prices is their availability in real time on a daily or weekly basis. We investigate whether mixed-frequency models can be used to take advantage of these rich data sets. We show that, among a range of alternative high-frequency predictors, changes in U.S. crude oil inventories produce substantial and statistically significant real-time improvements in forecast accuracy. The preferred mixed-data sampling (MIDAS) model reduces the mean-squared prediction error by as much as 16 percent compared with the no-change forecast and has statistically significant directional accuracy as high as 80 percent. This MIDAS forecast also is more accurate than a mixed-frequency real-time vector autoregressive 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.