Abstract: Science Set Journal of Economics Research

Abstract:
This paper presents a robust mathematical framework tailored to address the complexities of risk management in energy trading. Through the integration of probability theory, stochastic forecasting models, Value-at-Risk (VaR), Conditional Value-at-Risk (CVaR), and stress testing, this framework offers vital quantitative tools for comprehensively capturing uncertainties prevalent in contemporary energy markets. Implementation case studies validate the applicability of these methodologies across diverse sectors within energy trading. While these models enable robust analysis, effective risk management necessitates a balance between quantitative rigor and practical expertise. Future research directions focus on refining high-frequency risk modeling, examining crisis-related contagion, leveraging real options frameworks, and employing advanced machine learning techniques. The synthesis of statistical modeling and business acumen is imperative for crafting resilient energy trading strategies amid the dynamic uncertainties of global energy markets.