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.

翻译：与转型相关的金融市场日益面临资产价格突然重估、波动率升高以及异质性宏观金融冲击的挑战。在此类条件下，传统高斯-线性预测框架可能无法完整刻画化石能源、可再生能源、技术及公用事业类资产之间的依赖结构。本文旨在探究在控制重尾多变量线性动力学后，转型相关金融收益是否仍存在残差非线性可预测性。针对该问题，我们构建了一个结合学生t分布向量自回归与非线性递归残差学习架构的混合预测框架。实证分析涵盖六只主要交易所交易基金，分别代表广泛股票市场及关键转型敏感行业。研究结果表明，数据呈现显著偏离高斯-线性行为的特征，包括超额峰度、波动率聚类现象以及经济计量滤波后仍存在的非线性依赖。样本外预测实验显示，相较传统VAR模型、独立机器学习方法及其他混合规范，所提框架能够持续提升预测精度。在宏观金融压力时期——尤其是新冠危机及乌克兰相关能源冲击期间——预测增益更为显著。总体而言，研究结果表明，转型相关金融系统呈现区间依赖且重尾的预测动力学特征，此类特征无法被标准高斯-线性模型充分捕捉。