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One Great Pool or Many? Measuring Global Oil Market Integration Using a Dynamic Time Warping-Hierarchical Cluster Approach

Author

Listed:
  • Marc Gronwald
  • Luyao Zhu

Abstract

This paper measures global crude oil market integration using dynamic time warping combined with hierarchical cluster analysis. Using ten global crude oil prices (2000-2026), we find that markets cluster primarily by geography: West African, Middle Eastern, and U.S. markets each form distinct regional clusters: Brent occupies an intermediate position. A rolling window analysis reveals an increase in integration over time, but a remarkably stable regional structure. A subsample analysis shows that during the turbulent 2020-2024 period, integration increases but the clear geographical pattern weakens. Validation metrics such as silhouette width and cophenetic correlation confirm robust clustering across all periods.

Suggested Citation

  • Marc Gronwald & Luyao Zhu, 2026. "One Great Pool or Many? Measuring Global Oil Market Integration Using a Dynamic Time Warping-Hierarchical Cluster Approach," CESifo Working Paper Series 12723, CESifo.
    • Handle: RePEc:ces:ceswps:_12723
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    References listed on IDEAS

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    1. Cui, Jinxin & Maghyereh, Aktham, 2023. "Time-frequency dependence and connectedness among global oil markets: Fresh evidence from higher-order moment perspective," Journal of Commodity Markets, Elsevier, vol. 30(C).
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    Keywords

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    JEL classification:

    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • F15 - International Economics - - Trade - - - Economic Integration
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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