系统管理学报

系统管理学报 ›› 2025, Vol. 34 ›› Issue (2): 325-341.DOI: 10.3969/j.issn.2097-4558.2025.02.003

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中国冶金产业链与泛能源市场间风险传染——基于波动溢出网络视角

王彬洁1,2，薛建豪1,2，刘欣灵3，戴星宇1,2，单庄园1,2，王群伟1,2,4   

  1. 1.南京航空航天大学 经济与管理学院，南京 211106；2.南京航空航天大学 能源软科学研究中心，南京 211106；3.东南大学 经济管理学院，南京 211189；4.南京航空航天大学 制造系统数智管理与低碳运营实验室，南京 211106
  • 收稿日期:2024-03-11 修回日期:2024-08-12 出版日期:2025-03-28 发布日期:2025-04-15
  • 基金资助:
    国家社会科学基金资助项目（21&ZD110）；南京航空航天大学研究生科研与实践创新计划资助项目（xcxjh20240901）

Risk Contagion Between China’s Metallurgical Industry Chain and Pan-Energy Market: A Volatility Spillover Network Perspective

WANG Binjie1,2，XUE Jianhao1,2，LIU Xinling3，DAI Xingyu1,2，SHAN Zhuangyuan1,2，WANG Qunwei1,2,4   

  1. 1. College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; 2. Research Center for Soft Energy Science, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; 3. School of Economics and Management, Southeast University, Nanjing 211189, China; 4. Laboratory of Digital Intelligence Management and Low-Carbon Operations for Manufacturing System, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
  • Received:2024-03-11 Revised:2024-08-12 Online:2025-03-28 Published:2025-04-15

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摘要： 为研究中国冶金产业链与泛能源市场系统及其4类市场部门、28种市场个体的波动溢出网络特征，选取2014~2023年的行业指数和商品价格日度数据，结合DY溢出网络模型和图论理论，从全样本、滚动窗和多重时间尺度视角进行分析。实证结果表明：市场系统的总波动溢出量较小，但市场个体间溢出关系广泛；泛能源市场部门为波动溢出的净接收者，而冶金产业链中、下游市场部门为净输出者，且其波动溢出具有显著的时变特征；机械与钢铁市场向周边市场扩散波动风险的量最大，而锌和焦炭商品市场的波动风险传染范围最广；市场间波动溢出主要发生在超过5个交易日的中长周期，短周期内溢出效应不显著。研究可为冶金产业链与泛能源市场系统性风险管理提供新的视角。

关键词: 冶金产业链, 泛能源市场, 波动风险, DY溢出网络模型, 图论理论

Abstract: This paper explores the dynamics of volatility spillovers within a market system consisting of 28 market entities across four sectors of China’s metallurgical industry chain and the pan-energy market. Utilizing daily data on industry indices and commodity prices from 2014 to 2023, the analysis employs the DY spillover network model in combination with graph theory, considering full-sample, rolling-window, and multi-time scales perspectives. The empirical results reveal that while overall volatility spillovers within the system are relatively modest, the spillover relationships between market entities are extensive. Specifically, the pan-energy sector  serves as a net recipient of volatility spillovers, while the midstream and downstream sectors of the metallurgical industry chain act as net exporters, with spillover patterns exhibiting notable time-varying characteristics. The machinery and steel markets exhibit the highest magnitude of volatility spillover to neighboring markets, while zinc and coke markets demonstrate the most extensive contagion range in risk transmission. Spillovers are predominantly observed on medium-to-long-term time scales, with minimal effects detected on short-term time scales of five days or fewer. These findings contribute to a deeper understanding of systemic risk management within the metallurgical industry chain and pan-energy market.

Key words: metallurgical industry chain, pan-energy market, volatility risk, DY spillover network model, graph theory

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