China's Regional Financial Risk Spatial Correlation Network and Regional Contagion Effect: 2009-2016

Abstract

Abstract:

Based on regional financial risk stress index, this paper constructs the inter-provincial regional financial risk spatial correlation network from 2009 to 2016, uses social network analysis to examine the overall connectedness of regional financial risk spatial correlation network, and further carries out central analysis and block model analysis, and the effect network structure on the level of regional financial risks. The results show that, China's regional financial risk spatial association network is a typical "scale-free network" and associations in each province are unevenly distributed and featured by "small-world characteristics"; the degree of spatial correlation shows a rising trend in fluctuations and there are obvious spatial correlations and contagion effects in regional financial risks; for the entire financial risk related network, the increase in the degree of correlation will promote the spread of financial risks, increase the level of damage and scope of risk contagion. In the analysis of the effects of network structure characteristics, it is found that, for individual provinces, the increase in the degrees centrality, closeness centrality and betweenness centrality will reduce the level of financial risk. Therefore, the lower-ranking provinces should strengthen the association with other provinces and the provinces with higher central rankings should pay more attention to the prevention and management of financial risks to prevent systemic financial risks.

Key words: regional financial risk, spatial correlation, contagion effect, social network analysis

Shen Li, Liu Yuan, Li Wenjun. China's Regional Financial Risk Spatial Correlation Network and Regional Contagion Effect: 2009-2016[J]. , 2019, 31(8): 35-48.

References

[1] Kaufman G. G. Too Big to Fail in Banking:What Remains?[J]. Quarterly Review of Economics&Finance, 2002,42(3):423-436
[2] Diebold F. X., Yilmaz K. On the Network Topology of Variance Decompositions:Measuring the Connectedness of Financial Firms[J]. Journal of Econometrics, 2014,182(1):119-134
[3] 李红权,洪永淼,汪寿阳.我国A股市场与美股、港股的互动关系研究:基于信息溢出视角[J].经济研究, 2011,46(8):15-25
[4] 靳玉英,周兵.新兴市场国家金融风险传染性研究[J].国际金融研究, 2013,(5):49-62
[5] 蒋志平,田益祥,杜学锋.中国与欧美金融市场间传染效应的动态演变——基于欧债危机与次贷危机的比较分析[J].管理评论, 2014,26(8):63-73
[6] 苏木亚,郭崇慧.全球主要股票市场对我国股市的多渠道协同波动溢出效应——欧债危机背景下基于中证行业指数视角的研究[J].管理评论, 2015,27(11):21-32
[7] 黄玮强,庄新田,姚爽.基于信息溢出网络的金融机构风险传染研究[J].系统管理学报, 2018,27(2):235-243
[8] 马君潞,范小云,曹元涛.中国银行间市场双边传染的风险估测及其系统性特征分析[J].经济研究, 2007,42(1):68-78
[9] 沈丽,刘媛,李文君.不良贷款、空间溢出与区域经济增长[J].经济与管理评论, 2018, 34(3):26-41
[10] Anders C. J. China's Financial Market Integration with the World[J]. Journal of Chinese Economic and Business Studies, 2010, 8(3):293-314
[11] 周舟,董坤,汪寿阳.基于欧洲主权债务危机背景下的金融传染分析[J].管理评论, 2012,24(2):3-11
[12] 曹源芳,蔡则祥.基于VAR模型的区域金融风险传染效应与实证分析——以金融危机前后数据为例[J].经济问题, 2013,(10):59-64
[13] 吕徐莹.区域金融风险传染效应和扩散效应的实证分析[J].时代金融, 2016,(15):75-76
[14] 李岩柏,王学伦.区域性金融风险的积聚传导与防范控制——基于R市的实证研究[J].金融发展评论, 2017,(12):70-77
[15] 冯奇文.区域金融风险与地区经济发展的相关性研究[D].华中师范大学硕士学位论文, 2018
[16] 陈建青,王擎,许韶辉.金融行业间的系统性金融风险溢出效应研究[J].数量经济技术经济研究, 2015,32(9):89-100
[17] 宫晓琳.宏观金融风险联动综合传染机制[J].金融研究, 2012,(5):56-69
[18] 李刚,潘浩敏,贾威.金融危机传染路径的空间统计分析[J].统计研究, 2009,26(12):81-87
[19] 程棵,陆凤彬,杨晓光.次贷危机传染渠道的空间计量[J].系统工程理论与实践, 2012,32(3):483-494
[20] 李政,梁琪,涂晓枫.我国上市金融机构关联性研究——基于网络分析法[J].金融研究, 2016,(8):95-110
[21] 闫新国.基于相关与因果复杂网络的金融风险传染与冲击响应研究[D].湖南大学博士学位论文, 2017
[22] 王营,曹廷求.中国区域性金融风险的空间关联及其传染效应——基于社会网络分析法[J].金融经济学研究, 2017,32(3):46-55
[23] Illing M., Ying L. Measuring Financial Stress in a Developed Country:An Application to Canada[J]. Journal of Financial Stability, 2003,2(3):243-265
[24] Ravi B., Stephan D., Selim E. The Transmission of Financial Stress from Advanced to Emerging Economies[J]. Emerging Markets Finance&Trade, 2011,47(2):40-68
[25] 许悦.系统性压力综合指数的有效性研究[J].统计与决策, 2017,(2):166-170
[26] 张瑾.基于金融风险压力指数的系统性金融风险评估研究[J].上海金融, 2012,(9):53-57
[27] 陈雨露,马勇.构建中国的"金融失衡指数":方法及在宏观审慎中的应用[J].中国人民大学学报, 2013,27(1):59-71
[28] Rodríguez-Moreno M., Peña J. I. Systemic Risk Measures:the Simpler the Better?[J]. Journal of Banking&Finance, 2013,37(6):1817-1831
[29] 范小云,方意,王道平.我国银行系统性风险的动态特征及系统重要性银行甄别——基于CCA与DAG相结合的分析[J].金融研究, 2013,(11):82-95
[30] 许涤龙,陈双莲.基于金融压力指数的系统性金融风险测度研究[J].经济学动态, 2015,(4):69-78
[31] Hakkio C. S., Keeton W. R. Financial Stress:What Is It and How Can It Be Measured, and Why does It Matter?[J]. Economic Review-Federal Reserve Bank of Kansas City, 2009,94(2):5-50
[32] 陶玲,朱迎.系统性金融风险的监测和度量——基于中国金融体系的研究[J].金融研究, 2016,(6):18-36
[33] 刘华军,刘传明,孙亚男.中国能源消费的空间关联网络结构特征及其效应研究[J].中国工业经济, 2015,(5):83-95
[34] 李敬,陈旎,万广华,等."一带一路"沿线国家货物贸易的竞争互补关系及动态变化——基于网络分析方法[J].管理世界, 2017,(4):10-19
[35] 刘军.整体网分析——UCINET软件实用指南(第二版)[M].上海:格致出版社, 2014
[36] Freeman L. C., Roeder D., Mulholland R. R. Centrality in Social Networks:Ⅱ. Experimental Results[J]. Social Networks, 1980,2(2):119-141