Quantifying the role of interest rates, the Dollar and Covid in oil prices

Emanuel Kohlscheen

No 1040, BIS Working Papers from Bank for International Settlements

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 (RMSEs) 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.

Keywords: dollar; forecasting; machine learning; oil; risk. (search for similar items in EconPapers)
JEL-codes: C40 F30 Q40 Q41 Q47 (search for similar items in EconPapers)
Pages: 22 pages
Date: 2022-09
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ene
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Related works:
Working Paper: Quantifying the Role of Interest Rates, the Dollar and Covid in Oil Prices (2022)
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