Iran War Regime Change in Oil Markets

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The note answers two market questions:

  • How does one detect a structural break in global oil markets subjected to geopolitical shocks (e.g., the Iran War)?
  • When did the Iran War signal a regime change in global oil markets?

Signal Detection

The Bai-Perron (2003) procedure endogenously detects multiple, unknown structural breaks in time series data. It flags regime breaks by identifying when parameters in a linear model change over time, providing consistent estimates of break dates, even with heterogeneous errors.

Bai-Perron can be implemented in Python using the ruptures library, which offers off-line change point detection in the analysis and segmentation of non-stationary signals (C. Truong et al. (2020)).

Implemented algorithms include exact and approximate detection for various parametric and non-parametric models. ruptures focuses on ease of use by providing a well-documented and consistent interface. In addition, thanks to its modular structure, different algorithms and models can be connected and extended within this package.

The global nature of oil markets presents a range of possible time series signals to be considered:

  • WTI (West Texas Intermediate)
    • Very liquid US oil benchmark
    • Not directly exposed to Iran War risk
    • WTI has a history of decoupling from Middle East stress due to shale supply dynamics in the US.
  • Brent
    • Global seaborne benchmark with North Sea delivery
    • Captures the Europe/Asia demand dynamics and offers a better proxy for global supply disruption over WTI.
  • JCC (Japan Crude Cocktail)
    • Weighted average of crude imported into Japan
    • Dominated by Middle East sour grades
    • Published monthly reducing the timeliness of the signal.
  • Dubai/Oman
    • Physical Middle East benchmark sour crude.
    • Most directly exposed to Hormuz risk
    • However, Dubai/Oman is less liquid and is more difficult to access using public data (e.g., FRED api).

In considering the daily geopolitical risk of impending war strikes, we focused on 2 oil benchmarks available through FRED api:

  • Brent: global price signal
  • Brent-WTI spread: geopolitical premium signal isolating market demand fluctations. A sustained widening of Brent-WTI spread flags seaborne supply risk.

We considered three other signals:

  • VIX: financial system fear
  • 5-year breakeven inflation: the commodity pass-through to inflation expectations.
  • WTI Crude Volatility (OVX): the VIX equivalent for oil

Observations

We examined the period January 3, 2023 – April 30, 2026 comprising 860 observations. This period encompasses the current hostilities, the 2025 war strikes, and the Tariff shock.

The Bai-Perron algorithm flagged breaks in the time series data.

WindowBrent-WTIVIXGap (days)Notes
Apr-Jun 20252025-04-292025-06-24~56Tariff Shock
Aug 20252025-08-29––No VIX
Jan-Feb 20262026-01-082026-02-12~35Strong
Mar 20262026-03-122026-02-17~28Close

The Jan-Feb 2026 signal cluster report is consistent with persistent commodity chain effects.

The Brent-WTI spreads presents a dramatic structural brak spiking from ~$5.bbk baseline to $15-25/bbl.

The OVX confirms this spike suustaining coincidental elevation and peaking ~120.

The 2025 breaks appear minor.

We identified the onset of the Iran War Shock regime via the Bai-Perron structural break detection on the Brent-WTI spread, a direct proxy for Hormuz supply risk, with the boundary confirmed by concurrent VIX elevation (2026-02-12).

References

Bai, Jushan; Perron, Pierre (January 2003). “Computation and analysis of multiple structural change models”. Journal of Applied Econometrics. 18 (1): 1–22. doi:10.1002/jae.659. hdl:10.1002/jae.659

Truong, C; L. Oudre, L; Vayatis, N. Selective review of offline change point detection methods. Signal Processing, 167:107299, 2020. 

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