دانلود رایگان مقاله رابطه بین فرکانس بالا داده مالی و پیش بینی قیمت نفت

ABSTRACT

In recent years there has been an increased interest in the link between financial markets and oil markets, including the question of whether financial market information helps to forecast the real price of oil in physical markets. An obvious advantage of financial data in forecasting monthly oil prices is their availability in real time on a daily or weekly basis. We investigate the predictive content of these data using mixed-frequency models. We show that, among a range of alternative high-frequency predictors, cumulative changes in US crude oil inventories in particular produce substantial and statistically significant realtime improvements in forecast accuracy. The preferred MIDAS model reduces the MSPE by as much as 28% compared with the no-change forecast and has a statistically significant directional accuracy as high as 73%. This MIDAS forecast is also more accurate than a mixed-frequency real-time VAR forecast, but is not systematically more accurate than the corresponding forecast based on monthly inventories. We conclude that there is not typically much lost by ignoring high-frequency financial data in forecasting the monthly real price of oil.

5. Conclusion

We conclude that the best way of modelling mixedfrequency data in our context involves the use of MIDAS models rather than MF-VAR models. In general, the equal-weighted MIDAS model and the MIDAS model with estimated weights generate the most accurate real-time forecasts based on mixed-frequency data.We found no evidence that unrestricted MIDAS model forecasts are as accurate as or more accurate than forecasts from other MIDAS specifications. Based on these MIDAS models, we reviewed a wide range of high-frequency financial predictors of the real price of oil. The results can be classified as follows: • In many cases, the equal-weighted MIDAS model forecasts improve on the no-change forecast, but so does the corresponding forecast from a model including only lagged monthly data, and there is little to choose between the MIDAS model forecast and the forecast from the monthly model. Examples include models that incorporate weekly oil futures spreads, weekly gasoline product spreads, weekly returns on oil company stocks, and weekly US crude oil inventories.