Group detection in energy commodity markets through manifold-informed Wasserstein barycenter

Carlo Mari (), Tiziana Laureti () and Cristiano Baldassari ()
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Carlo Mari: University of Tuscia
Tiziana Laureti: University of Tuscia
Cristiano Baldassari: University of Tuscia

Quality & Quantity: International Journal of Methodology, 2025, vol. 59, issue 3, No 13, 2197-2227

Abstract: Abstract A novel approach based on unsupervised Machine Learning techniques is proposed to explore the complex interconnections between the dynamics of energy commodity prices, such as oil, gas and electricity prices in the USA, and the dynamics of certain macroeconomic variables that reflect the behavior of the US economy, such as interest rates and the Standard and Poor’s index. This methodology combines the Wasserstein barycenter with Graph Machine Learning and Manifold Learning techniques to identify common stochastic factors that drive the dynamics of energy commodity prices. Our analysis reveals the presence of a well-defined group of energy commodity markets that share similar characteristics. To study common stochastic factors, a Gaussian Mixture Model is fitted to the Wasserstein barycenter of the discovered cluster. The fitting is performed by maximum likelihood using the Expectation–Maximization algorithm with an initialization strategy based on Graph Machine Learning techniques. A fine-tuning of specific factors affecting the dynamics of energy commodity prices is also discussed.

Keywords: Wasserstein Barycenter; Network time series; Manifold learning; Graph embedding (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11135-025-02147-1

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