工作论文
当前位置:首页 > 工作论文
金融网络、关联性与系统性风险精准监管
阅读全文         下载全文
TitleFinancial Networks, Connectedness and Monitoring Systemic Risk Accurately  
作者范小云 史攀 王博  
AuthorFan Xiaoyun,Shi Pan and Wang Bo  
作者单位南开大学金融学院 
OrganizationNankai University 
作者Emailfanxiaoyun@vip.sina.com;1120180799@mail.nankai.edu.cn;nkwangbo@nankai.edu.cn 
中文关键词金融网络 系统性风险 MIDAS模型 精准监管 
Key WordsFinancial networks; Systemic risk; MIDAS model; Precise regulation 
内容提要数据的选择与处理对于系统性风险精准监管至关重要。本文对构建网络的原始数据进行了分解,然后在一个统一的框架下比较了市场网络与报表网络,系统分析了不同金融网络的网络图、网络中金融机构的系统重要性以及金融系统的整体关联性。研究发现:(1)市场网络强调了金融机构背后股东在网络中的作用,报表网络突出了金融机构规模的作用。(2)在样本期内,金融类金控集团下的金融机构系统重要性指数较低,央企金控集团下的金融机构的系统重要性指数较高。在金融动荡时期,民营类金控集团与地方政府类金控集团下的金融机构的系统重要性指数会显著升高。(3)规模与系统重要性不是一一对应的,在金融状况较为稳定的时期,仅仅极大规模的金融机构才具有高特征向量中心性;在金融动荡时期,小规模金融机构的特征向量中心性会显著升高。(4)滞后一季的市场网络表达的系统关联性指数与报表网络的指数走势大体一致,同时系统关联性指数对于美联储货币政策较为敏感。基于这些发现,我们建议监管当局应该综合利用两种网络进行精准监管,同时加强对大型金控集团的监管,在金融动荡期加强对中小银行的监管,密切关注外部冲击对我国金融稳定带来的影响。 
AbstractAfter the financial crisis of 2008, the connectedness of financial institutions has become an academic concern. However, the traditional banking model is difficult to describe the connectedness of financial institutions, so network analysis has become an important method in the study of systemic risk. Technically, there are two challenges to precision regulation. One is that networks constructing with different data will express different information. The other is that the data constructing networks will be driven by common factors and make the results incorrect. In order to meet the above challenges, we define two kinds of networks: market network based on market data and balance sheet network based on balance sheet data. We conduct an empirical study on 33 financial institutions listed in A-share stock market of China before December 31, 2010. Firstly, we refer to Hale & Lopez's (2019) method to decompose stock return and ROA by CAPM regression and MIDAS model respectively. Then we respectively use DY method (Demirer et al., 2018) and TMFG technology (Massara et al., 2016) to construct financial networks. Finally, we compare the graph, nodes’ systemic importance and the total connectedness in different networks. This paper contributes to the literature in three ways. Firstly, we compare market networks and balance sheet networks under a unified framework. Using the basic method of network analysis, we analyze the roles of financial holding groups in different periods and the dynamic changes of the systemic importance of financial institutions with different sizes. Secondly, we use mixed data sampling (MIDAS) model in the data processing of systematic risk research in this paper. The application of MIDAS model improves the accuracy of relevant indicators about systematic risk. Thirdly, we introduce Triangular Maximally Filtered Graph(TMFG)into the study of systemic risk for the first time. TMFG has advantages over other technologies such as Minimum Spanning Tree, which greatly improves the properties of the model. From the network analysis, we can draw the following conclusions: (1) While the market network emphasizes the role of shareholders behind the financial institutions in the network, the balance sheet network highlights the role of the size of financial institutions. (2) During the sample period, the systemic importance of financial institutions whose big shareholder is state-owned big banks or insurance companies is low, while the systemic importance of the financial institutions controlled by non-financial state-owned groups is high. Moreover, in the period of financial turbulence, the systemic importance of the financial institutions whose big shareholders is the private-controlled groups and the local government-controlled groups will significantly increase. (3) There isn’t a linear relationship between size and systemic risk. During periods of financial stability, only very large financial institutions have high eigenvector centrality. However, in the period of financial instability, the eigenvector centrality of small-scale financial institutions will increase significantly. (4) The systemic interconnectedness index expressed by the market network with a lag of one quarter generally follows the trend of that of the balance sheet network. What’s more, the systemic interconnectedness index is found to be relatively sensitive to the monetary policy of the federal reserve. Based on these findings, we propose four policy recommendations. Firstly, in order to improve regulatory efficiency and make correct decisions, regulatory authorities should make full use of different information provided by different networks. Secondly, regulators should strengthen the supervision of financial holding groups, including setting special requirements on the leverage ratio, strictly managing the equity structure, and strengthening the supervision of related transactions of financial holding groups. Thirdly, regulators should strengthen supervision of the largest financial institutions in times of financial stability. However, in times of financial instability the supervision of small and medium-sized financial institutions should be strengthened. Fourthly, regulators should pay close attention to the impact of external shocks, especially the monetary policy of the federal reserve, on China's financial stability. Regulators also should prevent systemic financial crisis by the fed's monetary policy adjustment.  
文章编号WP1447 
登载时间2019-12-09 
  • 主管单位:中国社会科学院     主办单位:中国社会科学院经济研究所
  • 经济研究杂志社版权所有 未经允许 不得转载     京ICP备10211437号
  • 本网所登载文章仅代表作者观点 不代表本网观点或意见 常年法律顾问:陆康(重光律师事务所)
  • 国际标准刊号 ISSN 0577-9154      国内统一刊号 CN11-1081/F       国内邮发代号 2-251        国外代号 M16
  • 地址:北京市西城区阜外月坛北小街2号   100836
  • 电话/传真:010-68034153
  • 本刊微信公众号:erj_weixin