Zhao Yiran, Gao Xiangyun, Wei Hongyu, Sun Xiaotian, An Sufang
School of Economics and Management, China University of Geosciences, Beijing 100083, China.
Key Laboratory of Carrying Capacity Assessment for Resource and Environment, Ministry of Natural Resources, Beijing 100083, China.
Entropy (Basel). 2024 Jun 27;26(7):549. doi: 10.3390/e26070549.
This study aims to employ a causal network model based on transfer entropy for the early warning of systemic risk in commodity markets. We analyzed the dynamic causal relationships of prices for 25 commodities related to China (including futures and spot prices of energy, industrial metals, precious metals, and agricultural products), validating the effect of the causal network structure among commodity markets on systemic risk. Our research results identified commodities and categories playing significant roles, revealing that industry and precious metal markets possess stronger market information transmission capabilities, with price fluctuations impacting a broader range and with greater force on other commodity markets. Under the influence of different types of crisis events, such as economic crises and the Russia-Ukraine conflict, the causal network structure among commodity markets exhibited distinct characteristics. The results of the effect of external shocks to the causal network structure of commodity markets on the entropy of systemic risk suggest that network structure indicators can warn of systemic risk. This article can assist investors and policymakers in managing systemic risk to avoid unexpected losses.
本研究旨在运用基于转移熵的因果网络模型对商品市场的系统性风险进行预警。我们分析了与中国相关的25种商品价格的动态因果关系（包括能源、工业金属、贵金属和农产品的期货及现货价格），验证了商品市场间因果网络结构对系统性风险的影响。我们的研究结果确定了发挥重要作用的商品和类别，表明工业和贵金属市场具有更强的市场信息传递能力，价格波动对其他商品市场的影响范围更广、力度更大。在经济危机和俄乌冲突等不同类型危机事件的影响下，商品市场间的因果网络结构呈现出不同特征。商品市场因果网络结构的外部冲击对系统性风险熵的影响结果表明，网络结构指标可以对系统性风险发出预警。本文可协助投资者和政策制定者管理系统性风险，避免意外损失。
