摘要
美国次贷危机之后,被国际三大主权信用评级机构均认定为"无风险"的政府债券规模已缩减超过60%,全球政府债务正在形成新的风险点且各国政府债务规模的差距呈现扩大的趋势,逐渐表现为区域性集中爆发的特征,并伴随跨国传导。基于关系数据分析范式,研究发现全球政府债务风险跨国传导呈现紧密性、连通性的空间关联网络结构特征,以瑞士、挪威、德国、荷兰、爱尔兰等为代表的欧洲国家处于全球政府债务风险跨国传导网络的中心地位;同时,地理位置和主权信用评级改变带来的信息溢出,会显著影响全球政府债务风险的跨国传导。因此,全球政府债务风险问题的解决,依赖于是否能够建立区域联合监管,调动各地区和部门的积极性,以实现联合预警、联合监管、联合防范的全球协同治理战略。
After the American subprime mortgage crisis,the sc ale of government bond that has been rated"no risks"by all the three major international sovereign credit rating agencies has been reduced by more than 60%.The global government debt is forming a new risk point and the gap between government debts among countries has widened,gradually showing the characteristics of regional concentrated outbreaks that accompanied by transnational transmission.Based on the related data analytical paradigm,the findings of the study show that the cross-border transmission of global government debt risk presents the characteristics of spatial correlation network structure with tightness and connectivity,with European countries represented by Switzerland,Norway,Germany,Holland,Ireland and so on at the center of the cross-border transmission network of global government debt risks.At the meantime,changes in geographical location and sovereign credit rating will result in information spillovers,which can significantly affect transnational transmission of global government debt risks.Therefore,the solution to the global government debt risk problems depends on whether the regional joint supervision can be established and the enthusiasm of various regions and departments can be mobilized,so as to achieve a global collaborative governance strategy of joint warning,joint supervision,and joint prevention.
作者
沈丽
刘媛
SHEN Li;LIU Yuan(Shandong University of Finance and Economics,Jinan 250014,China)
出处
《当代财经》
CSSCI
北大核心
2020年第4期38-51,共14页
Contemporary Finance and Economics
基金
国家社会科学基金项目“新常态初期区域金融风险生成机理及防控对策研究”(16BGL052)。
关键词
政府债务风险
跨国传导网络
协同治理
社会网络分析
government debt risk
transnational transmission network
collaborative governance
social network analysis
