Blockchain, IA et prévision de la volatilité pétrolière : vers un écosystème énergétique intelligent

Abstract

Purpose : This inquiry critically examines the intrinsic deficiencies of extant econometric paradigms in accurately forecasting crude oil price volatility, a challenge exacerbated by the escalating complexities of the global energy transition and the imperative for intelligent grid integration. We advance the hypothesis that a synergistic integration of advanced Artificial Intelligence (AI) and Blockchain technologies constitutes a superior framework for augmenting predictive fidelity and fostering market equilibrium.

Design/Methodology: Utilizing a rigorously simulated empirical methodology, this paper constructs and assesses a novel hybrid econometric-machine learning architecture. An ARMA-GARCH model is herein synergistically integrated with Long Short-Term Memory (LSTM) neural networks, designed to capture both linear and intricate non-linear dependencies within Brent/WTI crude oil price series from 2015 to 2025. Simultaneously, a qualitative investigation, executed via simulated content analysis (e.g., NVivo/Atlas.ti), scrutinizes the regulatory ramifications and transaction cost optimization afforded by Blockchain-enabled smart contracts in energy trading. The quantitative implementation is realized in Python (TensorFlow), whilst the qualitative dimension elucidates the institutional dynamics underpinning technological assimilation.

Findings: The simulated empirical outcomes unequivocally establish the superior predictive efficacy of the proposed hybrid AI-GARCH-LSTM model over conventional standalone GARCH specifications, evidenced by a statistically significant reduction in Root Mean Square Error (RMSE) for volatility forecasts. Moreover, the strategic integration of Blockchain technology, specifically through smart contracts, is demonstrably shown to accelerate settlement velocity and augment transactional transparency within simulated energy trading environments, thereby ameliorating counterparty risk and systemic operational inefficiencies. These observations collectively underscore the profound transformative capacity of an integrated AI-Blockchain framework in cultivating a more robust and epistemically predictable energy market.

Implications : The theoretical contribution resides in the articulation of a comprehensive analytical framework that bridges hitherto disparate paradigms—econometrics, machine learning, and institutional economics—to confront a critical financial and energy market exigency. Pragmatically, these insights furnish actionable intelligence for energy market participants, risk managers, and regulatory bodies. For market actors, the augmented predictive capabilities enable the formulation of more sophisticated hedging strategies and optimized portfolio allocations. For regulators, the findings delineate pathways for constructing resilient governance architectures for decentralized energy markets, particularly concerning the deployment and enforceability of smart contracts, thereby catalyzing the evolution towards a genuinely intelligent energy ecosystem.

Keywords : Crude Oil Price Volatility; Energy Markets; ARMA-GARCH ; LSTM Neural Networks ; Hybrid Econometric–Machine Learning Models; Artificial Intelligence ; Blockchain Technology; Smart Contracts; Energy Trading ; Financial Forecasting; Energy Transition ; Market Efficiency; Risk Management