Forecasting oil prices

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.