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

TL;DR

This paper tackles whether structurally interpretable econometric models can match machine learning in forecasting energy–macro dynamics while preserving causal interpretability. It develops a unified framework that combines Time-Varying Parameter SVAR (TVP-SVAR) with volatility and tail-dependence extensions (DCC- GARCH, t-copula, and mixed Archimedean copulas), and benchmarks against Gaussian Process Regression and other ML methods across seven macro-financial and energy series with Brent as the focal asset. The results show that TVP-SVAR, especially when augmented with copula-based dependence, delivers superior or comparable predictive accuracy to ML models, while uniquely providing impulse responses, regime diagnostics, and tail-risk measures. A key finding is predictive parity between copula-enhanced econometrics and GPR, suggesting ML can replicate structural dynamics but cannot substitute economic interpretation. The study highlights a productive synergy between econometrics and AI, offering a framework that informs energy policy and risk management by combining predictive power with structural insight.

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.

Figures (6)

• Figure 1: Structural stability diagnosis
• Figure 2: Impulse Response for BRENT
• Figure 3: RMSE-Based Predictive Performance: Copula-Enhanced Models vs. ML Hybrids
• Figure 4: Distributional Comparison of Forecast Errors: Copula-Based vs GPR Models
• Figure A1: Quantile-quantile plots of VAR model residuals