Nonlinear and Heavy-Tailed Predictability in Transition-Energy Financial Markets

Kpante Emmanuel Gnandi, Fredy Pokou () and Jules Sadefo Kamdem ()
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Kpante Emmanuel Gnandi: INSA Toulouse - Institut National des Sciences Appliquées - Toulouse - INSA - Institut National des Sciences Appliquées - Comue de Toulouse - Communauté d'universités et établissements de Toulouse
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

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Abstract: Transition-related financial markets are increasingly exposed to abrupt repricing episodes, elevated volatility, and heterogeneous macro-financial shocks. Under such conditions, conventional Gaussian-linear forecasting frameworks may provide an incomplete representation of the dependence structure linking fossil-energy, renewable-energy, technology, and utility-sector assets. This paper investigates whether transition-related financial returns exhibit residual non-linear predictability after controlling for heavy-tailed multivariate linear dynamics. To address this question, we develop a hybrid forecasting framework combining Student-t Vector Autoregressions with nonlinear recurrent residual learning architectures. The empirical analysis considers six major exchange-traded funds representing broad equity markets and key transition-sensitive sectors. The results reveal substantial departures from Gaussian-linear behavior, including excess kurtosis, volatility clustering, and remaining nonlinear dependence after econometric filtering. Out-of-sample forecasting experiments show that the proposed framework consistently improves predictive accuracy relative to conventional VAR models, standalone machine-learning methods, and alternative hybrid specifications. The forecasting gains become more pronounced during periods of macro-financial stress, particularly during the COVID-19 crisis and the Ukraine- related energy shock. Overall, the findings suggest that transition-related financial systems exhibit regime-sensitive and heavy-tailed predictive dynamics that are insufficiently captured by standard Gaussian-linear models alone.

Keywords: Transition finance; Energy-market dependenc; Heavy-tailed forecasting; Student-t VAR (search for similar items in EconPapers)
Date: 2026-05-24
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