Interpretable machine learning unveils nonlinear drivers of global energy risk spillovers: A TVP-VAR approach

Ditian Zhang, Pan Tang, Chun Tang and Xiaobing Lai

Economic Modelling, 2025, vol. 151, issue C

Abstract: This study examines global energy risk spillovers using a time-varying parameter vector autoregression (TVP-VAR) model and interpretable machine learning. Unlike previous studies constrained by single-factor analyses and linear assumptions, we resolve three key limitations: capturing multidimensional drivers, addressing multicollinearity, and modeling nonlinear dynamics. Our findings reveal that spillovers fluctuate temporally, driven by long-term components, with energy-rich and rapidly transforming economies as primary transmitters. Machine learning models outperform linear regression, identifying critical nonlinear interactions among economic development, energy structure, and balance of payments. Regional heterogeneity is pronounced: Europe and the U.S. prioritize economic growth, China focuses on capital flows, while Japan and Israel emphasize oil imports. By integrating interpretable ML with TVP-VAR, this study advances systemic risk analysis and provides policymakers with actionable, region-specific strategies for energy market stability.

Keywords: Energy markets; Risk spillover; Interpretable machine learning; Nonlinear relationships (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0264999325001737
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:151:y:2025:i:c:s0264999325001737

DOI: 10.1016/j.econmod.2025.107178

Access Statistics for this article

Economic Modelling is currently edited by S. Hall and P. Pauly

More articles in Economic Modelling from Elsevier
Bibliographic data for series maintained by Catherine Liu ().