Predictive Accuracy versus Interpretability in Energy Markets: A Copula-Enhanced TVP-SVAR Analysis

Fredy Pokou (), Jules Sadefo Kamdem () and Kpante Emmanuel Gnandi
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Fredy Pokou: MRE - Montpellier Recherche en Economie - UM - Université de Montpellier, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 - Centrale Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique
Jules Sadefo Kamdem: MRE - Montpellier Recherche en Economie - UM - Université de Montpellier
Kpante Emmanuel Gnandi: ENAC-LAB - Laboratoire de recherche ENAC - ENAC - Ecole Nationale de l'Aviation Civile

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Abstract: This paper investigates whether structural econometric models can rival machine learning in forecasting energy–macro dynamics while retaining causal interpretability. Using monthly data from 1999 to 2025, we develop a unified framework that integrates Time-Varying Parameter Structural VARs (TVP-SVAR) with advanced dependence structures, including DCC-GARCH, t-copulas, and mixed Clayton–Frank–Gumbel copulas. These models are empirically evaluated against leading machine learning techniques Gaussian Process Regression (GPR), Artificial Neural Networks, Random Forests, and Support Vector Regression across seven macro-financial and energy variables, with Brent crude oil as the central asset. The findings reveal three major insights. First, TVP-SVAR consistently outperforms standard VAR models, confirming structural instability in energy transmission channels. Second, copula-based extensions capture non-linear and tail dependence more effectively than symmetric DCC models, particularly during periods of macroeconomic stress. Third, despite their methodological differences, copula-enhanced econometric models and GPR achieve statistically equivalent predictive accuracy (t-test p = 0.8444). However, only the econometric approach provides interpretable impulse responses, regime shifts, and tail-risk diagnostics. We conclude that machine learning can replicate predictive performance but cannot substitute the explanatory power of structural econometrics. This synthesis offers a pathway where AI accuracy and economic interpretability jointly inform energy policy and risk management.

Keywords: Copula; DCC-GARCH; Gaussian Process Regression; Nonlinear dependence structure; Financial Shocks; Interpretability; TVP-SVAR (search for similar items in EconPapers)
Date: 2026-01-26
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