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复杂系统与复杂性科学  2020, Vol. 17 Issue (2): 11-21    DOI: 10.13306/j.1672-3813.2020.02.002
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基于复杂网络的银行波动溢出效应研究
毛昌梅1, 3, 韩景倜2, 3, 刘举胜2, 3
1. 申万宏源证券有限公司博士后科研工作站,上海 200031;
2. 上海财经大学信息管理与工程学院,上海 200433;
3. 上海金融智能工程技术研究中心,上海 200433
Volatility Spillover Effect of Chinese Listed Commercial Banks-Based on Complex Network
MAO Changmei1, 3, HAN Jingti2, 3, LIU Jusheng2, 3
1. Postdoctoral Research Station, Shenwan Hongyuan Securities Co. Ltd., Shanghai 200031, China;
2. School of information management and engineering, Shanghai University of Finance and Economics, Shanghai 200433,China;
3. Shanghai Financial Intelligent Engineering Technology Research Center,Shanghai 200433,China
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摘要 为了探究中国商业银行系统性风险,基于信息溢出视角,选取中国14家上市商业银行的日收益率为研究对象,按照重大金融事件“钱荒”,“股灾”将数据分为3个阶段,首先运用BEKK-GARCH模型构建了波动溢出网络,通过分析网络的拓扑指标探究了银行波动网络的溢出效应,最后以波动网络为例,利用目标免疫和随机免疫策略对所构建的网络进行了稳健性检验。研究表明:1)在不同阶段下,中国上市商业银行波动溢出网络具有不同的网络结构,风险的冲击可以使得银行之间的联系更加紧密;2)中国上市商业银行之间风险溢出网络在高风险时期,网络集聚系数呈现明显增大趋势,网络的平均路径呈现明显缩短趋势,这一特点表明高风险时期各银行会紧密联系共同抵御风险;3)目标免疫对银行波动溢出网络稳定的影响远大于随机免疫的影响。
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毛昌梅
韩景倜
刘举胜
关键词 波动溢出效应银行网络BEKK-GARCH稳健性    
Abstract:In order to explore the systemic risk of China's commercial banks, this paper considers the impact of positive and negative market news on the bank's network structure and the complexity of systemic risk in financial institutions, based on the perspective of information spillovers. Firstly, it selected the daily rate of return of listed 14 commercial banks in China. Then, the data is divided into three stages according to major financial events “money shortage” and “stock disaster”. Secondly, it used the complex network method to construct the shock network and the volatility overflow network based on the BEKK-GARCH model, and it explored the wave spillover effect and linkage effect of the bank's wave network by analyzing the network's indicators. Finally, it selected the volatility overflow network as an example, and used the target network and the random immune strategy to do a robustness test . The research result shows that: 1) at different stages, bank volatility spillover networks have different network structures, and the impact of risks can make banks more closely; 2)when the risk spillover network in a high-risk zone system, the network agglomeration coefficient is increasing, and the average path of the network is shortening significantly, this feature indicates that banks in the spillover network will be closely linked to resist risks; 3)the impact of target immunity on network stability is much greater than that of random immunity.
Key wordsvolatility spillover effects    banking network    BEKK-GARCH    robustness
     出版日期: 2020-06-24
ZTFLH:  F830.1  
基金资助:国家社科基金重大项目(18ZDA088);国家自然科学基金项目(71871144);上海市科委“科技创新行动计划”高新技术领域项目(18DZ1112103);上海财经大学 2019 年研究生创新基金(CXJJ2019400)
通讯作者: 韩景倜(1959),男,陕西西安人,博士,教授,主要研究方向为金融科技,大数据挖掘。   
作者简介: 毛昌梅(1989),女,安徽天长人,博士,主要研究方向为信用风险,大数据分析。
引用本文:   
毛昌梅, 韩景倜, 刘举胜. 基于复杂网络的银行波动溢出效应研究[J]. 复杂系统与复杂性科学, 2020, 17(2): 11-21.
MAO Changmei, HAN Jingti, LIU Jusheng. Volatility Spillover Effect of Chinese Listed Commercial Banks-Based on Complex Network. Complex Systems and Complexity Science, 2020, 17(2): 11-21.
链接本文:  
http://fzkx.qdu.edu.cn/CN/10.13306/j.1672-3813.2020.02.002      或      http://fzkx.qdu.edu.cn/CN/Y2020/V17/I2/11
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[1] 陈志英, 肖忠意, 李永奎. 国际股市隐含波动率溢出效应分析[J]. 复杂系统与复杂性科学, 2019, 16(4): 56-65.
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