中国金融机构波动溢出动态关联网络研究
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摘要
基于VAR-GARCH-BEKK模型,构建金融机构波动溢出动态关联网络,分析网络的关联拓扑结构指标,挖掘金融行业间的波动溢出动态关联规律.结果表明:银行业内部金融机构间的波动溢出水平比较稳定;绝大多数时期,银行业和证券业金融机构在网络中表现为独立的关联"社团";从受到外部波动溢出影响方面看,证券业的平均系统关联最强,信托业的平均跨行业关联最强;从对外波动溢出影响方面看,各行业的平均系统关联强弱差异不大,保险业的平均跨行业关联最强;从网络总体"嵌入"程度看,信托业的平均系统关联及跨行业关联均最强,其网络"嵌入"程度最深.
A dynamic volatility spillover netw ork of financial institutions was constructed on the basis of the VAR-GARCH-BEKK model,whose associated topology indicators were analyzed,and the dynamic correlation law s of volatility spillover betw een financial industries were investigated. The results showed that the volatility spillover levels are more stable in the bank industry than those in other industries. During most of the periods,the banks and security companies form independent correlated communities. From the perspective of receiving volatility spillover,the security industry has the strongest average systemic correlation,and the trust industry has the strongest average inter-industry correlation. From the perspective of sending volatility spillover,the insurance industry has the strongest average inter-industry correlation,and the average systemic correlations are not significantly different among different industries. From the perspective of netw ork embeddedness,the trust industry has the strongest average systemic correlation and inter-industry average correlation,whose embeddedness is the greatest.
A dynamic volatility spillover netw ork of financial institutions was constructed on the basis of the VAR-GARCH-BEKK model,whose associated topology indicators were analyzed,and the dynamic correlation law s of volatility spillover betw een financial industries were investigated. The results showed that the volatility spillover levels are more stable in the bank industry than those in other industries. During most of the periods,the banks and security companies form independent correlated communities. From the perspective of receiving volatility spillover,the security industry has the strongest average systemic correlation,and the trust industry has the strongest average inter-industry correlation. From the perspective of sending volatility spillover,the insurance industry has the strongest average inter-industry correlation,and the average systemic correlations are not significantly different among different industries. From the perspective of netw ork embeddedness,the trust industry has the strongest average systemic correlation and inter-industry average correlation,whose embeddedness is the greatest.
引文
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[3]Elliott M,Golub B,Jackson M O. Financial networks and contagion[J]. American Economic Review,2014,104(10):3115-3153.
[4]Greenwood R,Landier A,Thesmar D. Vulnerable banks[J].Journal of Financial Economics,2015,115(3):471-485.
[5]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.
[6]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.(Li Zheng,Liang Qi,Tu Xiao-feng. The connectedness of Chinese listed financial institutions:a study based on network analysis[J]. Journal of Financial Research,2016(8):95-110.)
[7]文风,汪洋.我国上市银行系统关联性研究——基于方差分解的网络拓扑分析[J].金融与经济,2017(2):64-70.(Wen Feng,Wang Yang. The systemic connectedness of Chinese listed banks—an network topology analysis based on variance decompositions[J]. Journal of Finance and Economics,2017(2):64-70.)
[8]Billio M,Getmansky M,Lo A W,et al. Econometric measures of connectedness and systemic risk in the finance and insurance sectors[J]. Journal of Financial Economics,2012,104(3):535-559.
[9]Yang J,Zhou Y. Credit risk spillovers among financial institutions around the global credit crisis:firm-level evidence[J]. Management Science,2013,59(10):2343-2359.
[10]Huang W Q,Zhuang X T,Yao S,et al. A financial network perspective of financial institutions'systemic risk contribution[J]. Physica A,2016,456:183-196.
[11]Engle R F,Kroner K F. Multivariate simultaneous generalized ARCH[J]. Econometric Theory,1995,11(1):122-150.
[12]Andersen T G,Davis R A,Kreib J P,et al. Handbook of financial time series[M]. Berlin:Springer,2009.