Trump Tariffs and Persistence in Crude Oil Prices: A Long-Memory Approach
(Pages 133-143)
Guglielmo Maria Caporale1, Luis Alberiko Gil-Alana2 and Oluwadare O. Ojo3
1Department of Economics, Finance and Accounting, Brunel University of London, UK;
2Faculty of Economics and Business, University of Navarra, Pamplona, Spain;
3Department of Statistics, Federal University of Technology Akure, Nigeria
DOI: https://doi.org/10.55365/1923.x2026.24.12
Abstract:
This paper examines the impact on crude oil prices of the trade tariffs announced by the Trump administration on 2 April 2025 (“Liberation Day”). More specifically, it uses fractional integration methods to analyse daily data on WTI, Brent and Murban oil prices spanning the period from 3 June 2024 to 14 January 2026 for the former two and from 8 October 2024 to 15 January 2026 for the latter. The inclusion of WTI, Brent, and Murban provides a comparative perspective across major regional crude oil benchmarks, making it possible to assess whether the observed persistence is global or benchmark-specific. Their long-memory and persistence properties are investigated initially over the full sample, and then the effects of the Trump tariff announcement are assessed by means of subsample analysis for the pre- and post announcement period as well as recursive estimation of the fractional differencing parameter d measuring persistence. The results indicate that the unit root null cannot be rejected in any case, whether one considers the full sample or the subsamples, which implies that shocks have permanent effects. Further, the recursive estimation shows a transient loss of precision or instability around the announcement, with no detectable change in persistence as implied by the wide confidence bands.
Keywords:
crude oil prices, trade shocks, Trump tariffs, fractional integration, long memory, persistence
JEL Classification:
C22, F10, F13.
How to Cite:
Guglielmo Maria Caporale, Luis Alberiko Gil-Alana and Oluwadare O. Ojo. Trump Tariffs and Persistence in Crude Oil Prices: A Long-Memory Approach. [ref]: vol.24.2026. available at: https://refpress.org/ref-vol24-a12
Licensee REF Press This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
