Extreme risk measurement for the oil and China’s sectors system—network-based approach and machine learning methods

IF 1.9 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Frontiers in Physics Pub Date : 2023-12-15 DOI:10.3389/fphy.2023.1292418

Tingwei Fang, Dong Wang, Zhijia Lin, Xiaofan Wang

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Abstract

China is a large oil-consuming country, and sharp fluctuations in oil prices are bound to be detrimental to the stable growth of its economy. Therefore, accurately grasping the impact of the oil market on China’s sectors is the key to ensuring its healthy economic development. The article aims to explore the extreme risk transfer link of the oil and China’s sectors system, focusing on uncovering the risk spillover mechanism of the oil and providing early warning on it. We apply the TENET method to discuss risk propagation relationships within the oil and sectors system at three levels. The TVP-VAR model is brought in to recognize the factors affecting risk spillover in the oil market from the network correlation perspective. Finally, early warning of oil risk spillover is provided by incorporating the influencing factors into a machine learning model. The outcomes indicate that the risk connectivity of the oil and China’s sectors system is highly correlated with extreme events. There are variations in the spillover effects of oil market risk on different sectors, with Telecommunication Services, Utilities, Financials and Major Consumer sectors being the main bearers of the oil risk shocks. Overall, oil risk spillovers are mainly driven by economic policy and geopolitics, but oil price uncertainty is found to have a persistent impact on oil market risk spillovers in the dynamic analysis. Random forest model can provide effective early warning of oil risk spillovers. In addition, the significance analysis shows that oil price uncertainty and inflation are important factors affecting oil risk spillovers and are nonlinearly correlated with them.

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石油和中国行业系统的极端风险测量--基于网络的方法和机器学习方法

中国是石油消费大国，石油价格的剧烈波动必然不利于中国经济的稳定增长。因此，准确把握石油市场对中国各行业的影响，是确保中国经济健康发展的关键。本文旨在探讨石油与中国各行业系统的极端风险传递联系，重点揭示石油的风险溢出机制并对其进行预警。我们运用 TENET 方法从三个层面探讨了石油与行业系统内部的风险传播关系。引入 TVP-VAR 模型，从网络相关性角度认识影响石油市场风险溢出的因素。最后，通过将影响因素纳入机器学习模型，对石油风险溢出进行预警。研究结果表明，中国石油行业体系的风险连通性与极端事件高度相关。石油市场风险对不同行业的溢出效应存在差异，电信服务、公用事业、金融和主要消费行业是石油风险冲击的主要承受者。总体而言，石油风险溢出效应主要由经济政策和地缘政治驱动，但在动态分析中发现油价不确定性对石油市场风险溢出效应具有持续影响。随机森林模型可以对石油风险溢出效应进行有效预警。此外，显著性分析表明，油价不确定性和通货膨胀是影响石油风险溢出效应的重要因素，并与之非线性相关。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Frontiers in Physics Mathematics-Mathematical Physics

CiteScore

4.50

自引率

6.50%

发文量

1215

审稿时长

12 weeks

期刊介绍： Frontiers in Physics publishes rigorously peer-reviewed research across the entire field, from experimental, to computational and theoretical physics. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, engineers and the public worldwide.