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This study analyses oil price movements through the lens of an agnosticrandom forest model, which is based on 1,000 regression trees. It shows thatthis highly disciplined, yet flexible computational model reduces in sampleroot mean square errors by 65% relative to a standard linear least square modelthat uses the same set of 11 explanatory factors. In forecasting exercises theRMSE reduction ranges between 51% and 68%, highlighting the relevance of nonlinearities in oil markets. The results underscore the importance ofincorporating financial factors into oil models: US interest rates, the dollarand the VIX together account for 39% of the models RMSE reduction in the post2010 sample, rising to 48% in the post 2020 sample. If Covid 19 is alsoconsidered as a risk factor, these shares become even larger.

Quantifying the Role of Interest Rates, the Dollar and Covid in Oil Prices