基于复杂网络的新能源汽车行业信用风险传染机制研究  

Study on Credit Risk Contagion Mechanism of Green-car Industry Based on Complex Network

作　　者：刘妍 张红欣 LIU Yan;ZHANG Hongxin(School of Economics,Ocean University of China,Qingdao 266100,China)

机构地区：[1]中国海洋大学经济学院,山东青岛266100

出　　处：《运筹与管理》2023年第10期144-150,共7页Operations Research and Management Science

基　　金：国家自然科学基金青年项目(72003180)。

摘　　要：在“后补贴时代”背景下,新能源汽车行业信用风险传染成为加剧行业系统性风险波动的关键特征。本文利用2016—2020年新能源汽车行业违约距离数据,运用复杂网络中最小生成树(MST)方法刻画行业信用风险传染机制,研究发现:(1)新能源汽车行业信用风险网络具备典型的“无标度”及“小世界”特征,企业信用风险关联分布不均;(2)网络关联集中度呈波动上升态势,较高的关联集中度体现行业信用风险关联结构紧密性特征进而成为风险全局性传染现象的催化剂;(3)行业信用链风险关联网络异于金融风险网络的突出特点是其拓扑呈现出上、中、下游分环节聚类的社团结构特征,且不同外部政策环境下各聚类间的联动效应有所差异,这种环节内聚类及聚类间的联动是信用风险内生性网络传染的重要机制。基于上述发现,本文提出“充分利用两类企业的优势地位、政府‘监’与市场‘控’协同、进行不同市态阶段下分环节动态监控”的建议,为防范行业信用风险传染演变为系统性风险提供决策参考。At present,enterprises form complex relationships with network characteristics based on creditor’s rights and debts,guarantees and cross-shareholdings,etc.The“credit chain”which is inherent in the correlation relationship has gradually become an important risk infection path among enterprises.The credit chain formed by the association relationship of a single enterprise’s credit risk spreads and enlarges in the industry,which is the credit risk contagion.The outstanding feature of the new energy automobile industry is the upper,middle and downstream“deep linkage”.The scope of risk impact of a single enterprise is no longer limited to the enterprise itself.Through the network of mutual credit support path overflow to the upper and lower credit related parties,the industry credit risk correlation has gradually increased.Based on this,we adopt the complex network method to study the credit risk transmission mechanism of the green-car industry,which on the one hand makes a useful supplement to the relevant achievements of the credit risk transmission of green-car industry,and on the other hand has a strong guiding significance to prevent the industry credit risk transmission from evolving into a systemic risk.Based on the above background,this paper selects 25 representative enterprises in the new energy automobile industry as research objects,and the data source is the financial data of enterprises in each year in the Wind database.In this paper,the default distance in the KMV model is used to measure the credit risk level of each enterprise in different years,and the Pearson coefficient is used to measure the credit risk correlation degree of each enterprise in different years,and on this basis,the industry credit risk threshold network is constructed.Finally,the evolution of network characteristics is studied in stages through the complex network model.In the aspect of network research,first of all,degree centrality,network centralization,average path length and clustering coefficient are used to descr

关 键 词：复杂网络理论 新能源汽车行业 信用风险传染 KMV模型 最小生成树 

分 类 号：F832[经济管理—金融学]