Journal of Energy Markets

Kostas Andriosopoulos
Alba Graduate Business School

As the energy transition accelerates, the dynamics of energy markets are evolving, market structures are becoming more complex and there is ongoing geopolitical uncertainty. The ever-expanding integration of renewable energy sources, the implementation of carbon pricing mechanisms and fluctuating geopolitical conditions are effectively reshaping how energy markets operate. Recent geopolitical disruptions have also highlighted the importance of assessing market volatility, improving forecasting techniques and evaluating market efficiency to support informed decision-making across the energy sector. The research papers presented in this issue of The Journal of Energy Markets offer new insights into price dynamics, volatility behavior and the implications of geopolitical instabilities for financial and energy markets.

This issue offers readers the opportunity to reflect on a fundamental issue: technological innovation, investments in infrastructure and the ability to prepare for uncertainty will determine the future resilience of energy systems. In an increasingly volatile world, data-driven modeling, predictive analytics and interdisciplinary research are essential for enhancing market resilience, supporting evidence-based policymaking and strengthening energy security.

The first paper in this issue, “Predicting Chinese carbon prices and influence factors: evidence from quantile shrinkage methods” by JiaWang and RuofeiWang, aims to support informed investment decision-making by policy makers and market participants, enabling them to forecast Chinese carbon prices. The authors utilize a variety of predictors and analyze their impact across various carbon market conditions and emergency (“black swan”) events. They present statistical models including quantile shrinkage methodologies, machine learning techniques, traditional econometric and dimension reduction methodologies, with 18 technical indicators. Their findings reveal that using a rolling window forecasting approach, the quantile group least absolute shrinkage and selection operator (LASSO) model exhibits better prediction accuracy than other models. These results are shown to be robust through testing on alternative carbon markets.

In “Geopolitical shocks and market memory: evidence from crypto and energy assets during the Russo-Ukrainian war”, the second paper in the issue, Hassen Raïs and Assen Slim examine the impact of the Russo-Ukrainian war on the informational efficiency of cryptocurrency financial markets and energy stocks through a time-series model. They examine daily data for selected energy stocks and major cryptocurrencies from October 2021 to December 2024, a period that straddles the onset and continuation of the conflict. By analyzing long-term dependencies in both returns and volatility, they identify persistent shifts in market behavior that challenge the random walk hypothesis. Their results highlight a structural increase in price predictability and volatility persistence during conflict conditions, suggesting that periods of geopolitical instability and tensions have implications for forecasting strategies, arbitrage opportunities and portfolio allocation. Any geopolitical crisis can fundamentally alter market dynamics. This paper demonstrates that geopolitical shocks can reshape the memory structure of financial markets.

Our third and final paper in this issue is “The variance-Hawkes process and its application to energy markets” by Jessica McGillivray and Anatoliy Swishchuk. It introduces a novel stochastic process – the variance-Hawkers process – that combines a Hawkers process with Brownian motion to more effectively encode stochastic volatility and clustering in financial model behavior. While calibrating the model, the authors show that it is a good fit for 2018 and 2019 New York Mercantile Exchange natural gas and West Texas Intermediate crude oil front-month futures log returns. Simulating the square-of-variance Hawkes process with its Itˆo formula through multiple runs offers a simple yet powerful approach for incorporating clustering effects into financial models, creating a tool for pricing and risk management as well as for research into more complex models.

The papers in this issue converge toward a common conclusion: as energy markets undergo digitalization and become more uncertain and interconnected, understanding their dynamics requires increasingly sophisticated analytical frameworks. The interactions between energy systems, geopolitical events and financial markets demand tools that can identify nonlinear relationships, structural changes and evolving market behaviors more efficiently. Volatility modeling, carbon price forecasting and research into the implications of geopolitical instabilities on market efficiency all contribute alternative approaches that help to capture the complexity of today’s energy landscape.