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

TL;DR

This paper employs a random forest model to analyze oil price movements, demonstrating significant improvements over linear models and highlighting the influence of financial factors and Covid-19 on oil prices.

Contribution

It introduces a flexible random forest approach to quantify the impact of financial and pandemic factors on oil prices, outperforming traditional linear models.

Findings

Random forest reduces RMSE by up to 68% compared to linear models.

Financial factors explain up to 48% of RMSE reduction post-2020.

Including Covid-19 as a risk factor increases the explained variance.

Abstract

This study analyses oil price movements through the lens of an agnostic random forest model, which is based on 1,000 regression trees. It shows that this highly disciplined, yet flexible computational model reduces in sample root mean square errors by 65% relative to a standard linear least square model that uses the same set of 11 explanatory factors. In forecasting exercises the RMSE reduction ranges between 51% and 68%, highlighting the relevance of non linearities in oil markets. The results underscore the importance of incorporating financial factors into oil models: US interest rates, the dollar and the VIX together account for 39% of the models RMSE reduction in the post 2010 sample, rising to 48% in the post 2020 sample. If Covid 19 is also considered as a risk factor, these shares become even larger.