Developing Derivative-Based Hedging Strategies to Manage Volatility in Energy Market Prices

Oluwole Oluwadamilola Agbede, Experience Efeosa Akhigbe, Ajibola Joshua Ajayi and Nnaemeka Stanley Egbuhuzor
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Oluwole Oluwadamilola Agbede: Booth School of Business, University of Chicago, IL, USA
Experience Efeosa Akhigbe: Booth School of Business, University of Chicago, IL, USA
Ajibola Joshua Ajayi: The Wharton School of Business, University of Pennsylvania, PA, USA
Nnaemeka Stanley Egbuhuzor: Columbia Business School, Columbia University, NY, USA

International Journal of Research and Innovation in Social Science, 2025, vol. 9, issue 2, 1651-1672

Abstract: The energy market is inherently volatile, driven by factors such as geopolitical events, supply-demand imbalances, and macroeconomic uncertainties. This volatility poses significant financial risks for market participants, including energy producers, consumers, and investors. Developing effective hedging strategies is critical to mitigating these risks and ensuring financial stability. Derivative-based hedging, using instruments such as futures, options, and swaps, offers a structured approach to manage price fluctuations in the energy market. This study explores the development and optimization of derivative-based hedging strategies tailored to address the complexities and dynamics of energy market volatility. The research begins by analyzing the fundamental drivers of price volatility in energy markets, including the impact of seasonal variations, geopolitical tensions, and technological advancements in renewable energy. It highlights the role of derivatives in providing price stability and enabling long-term planning for stakeholders. The study develops a framework that combines financial theory with quantitative modeling techniques to design hedging strategies aligned with varying risk appetites and market conditions. The framework incorporates tools such as Value at Risk (VaR), stress testing, and scenario analysis to assess the effectiveness of hedging instruments in diverse market scenarios. In addition, the research evaluates the performance of key derivative instruments, focusing on their suitability for managing specific types of energy risks, such as crude oil, natural gas, and electricity prices. The study leverages historical data and machine learning algorithms to predict price trends, enabling more informed decision-making in derivative transactions. Case studies from global energy markets are utilized to illustrate the practical application of these strategies and their potential to safeguard financial outcomes in turbulent conditions. The findings of this study underscore the importance of derivative-based hedging as a cornerstone of risk management in energy markets. It provides actionable insights for market participants, policymakers, and financial institutions to enhance market stability and resilience. By integrating advanced analytical techniques with robust hedging frameworks, this research contributes to the broader goal of achieving sustainable and efficient energy market operations amidst ongoing volatility.

Date: 2025
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