Qi Xin, Zhao Tianyu
Institute of Chinese Financial Studies, Southwestern University of Finance and Economics, Chengdu, China.
School of Finance, Shanghai University of Finance and Economics, Shanghai, China.
PLoS One. 2025 May 23;20(5):e0322462. doi: 10.1371/journal.pone.0322462. eCollection 2025.
Taking top global energy companies as the epitome, this paper investigates the risk formulation mechanism of the international energy market under the impact of large shocks. We first use the machine learning method in (Liu and Pun, 2022) to calculate the systematic risk level - EMES - for each energy company. Then use network analysis methods to explore the internal risks due to risk comovement among top energy companies. Finally, a dynamic quantile regression model(DNQR) is used to investigate the external risks occasioned by network effects, individual company characteristics, and market environment. Our research finds that the method we use can capture the risk profile of the energy market under different major shocks. Secondly, we find that the risk contagion in the energy market exhibits geographical clustering characteristics, and certain firm-specific factors and market environmental factors of the company have a significant impact on the tail risk of the company. Our research can provide reference and guidance for risk management in the energy market.
以全球顶级能源公司为缩影，本文研究了在重大冲击影响下国际能源市场的风险形成机制。我们首先使用（Liu和Pun，2022）中的机器学习方法来计算每家能源公司的系统风险水平——EMES。然后使用网络分析方法来探究顶级能源公司之间风险联动所产生的内部风险。最后，使用动态分位数回归模型（DNQR）来研究由网络效应、个别公司特征和市场环境所引发的外部风险。我们的研究发现，我们所使用的方法能够捕捉不同重大冲击下能源市场的风险状况。其次，我们发现能源市场中的风险传染呈现出地理集聚特征，并且公司特定的某些因素和市场环境因素对公司的尾部风险有重大影响。我们的研究可为能源市场的风险管理提供参考和指导。
