基于网络视角的银行业系统性风险度量方法

doi:10.16381/j.cnki.issn1003-207x.2016.05.007

中国管理科学 ›› 2016, Vol. 24 ›› Issue (5): 54-64.doi: 10.16381/j.cnki.issn1003-207x.2016.05.007

基于网络视角的银行业系统性风险度量方法

隋聪^1,2, 谭照林¹, 王宗尧³

1. 东北财经大学金融学院, 辽宁大连 116025;
2. 东北财经大学商品市场与行为决策研究中心, 辽宁大连 116025;
3. 东北财经大学萨里国际学院, 辽宁大连 116025

收稿日期:2014-12-19 修回日期:2015-12-07 出版日期:2016-05-20 发布日期:2016-05-24
通讯作者: 隋聪(1978-),男(汉族),辽宁沈阳人,东北财经大学金融学院博士,副教授,硕士生导师,研究方向:银行系统性风险、网络模型、金融工程、银行风险管理,Email:suicong2004@163.com. E-mail:suicong2004@163.com
基金资助:
国家自然科学基金项目(71571034,61304180);教育部人文社会科学基金项目(12YJCZH211);辽宁省高等学校优秀人才支持计划资助项目(WJQ2015012)

A Network Perspective Measurement Method for Banking Systemic Risk

SUI Cong^1,2, TAN Zhao-lin¹, WANG Zong-yao³

1. School of Finance, Dongbei University of Finance and Economics, Dalian 116025, China;
2. Center for Commodity Markets and Behavioral Decision Research, Dongbei University of Finance and Economics, Dalian 116025, China;
3. Surry International Institute, Dongbei University of Finance and Economics, Dalian 116025, China

Received:2014-12-19 Revised:2015-12-07 Online:2016-05-20 Published:2016-05-24

摘要/Abstract

摘要： 网络模型已经成为研究银行系统性风险的重要方法。然而现有研究忽视了银行系统性风险的小概率特点,同时也缺少度量银行系统性风险的统一标准。为此,本文提出了基于网络模型的银行系统性风险度量方法:银行系统性风险VaR和银行系统性风险ES。首先,本文采用蒙特卡洛模拟方法,模拟银行外部冲击造成银行间网络损失的大样本。在银行间网络损失大样本中,估计银行系统性风险VaR和银行系统性风险ES。这两个测度能够捕捉到银行间网络损失的尾部特征,解决了对比随机冲击结果无法反映银行系统性风险的问题。其次,在模拟实验中,本文利用真实银行间网络结构参数,对模拟的三种银行间网络进行校准,保证了研究结论真实性和可靠性。最后,在模拟实验中发现:(1)外部冲击会引发违约传染的连锁反应,并导致银行间网络损失分布从近似正态分布转变成尖峰厚尾分布,最后变成双峰分布。(2)网络集中度越高发生违约传染连锁反应的概率越小,但是传染的破坏力会更大。(3)银行间网络的潜在传染作用会极大的放大银行系统的风险,而且违约传染效应是呈指数增长的。

关键词: 系统性风险, 银行间网络, 蒙特卡洛模拟, VaR

Abstract: The interbank network is convenient for liquidity adjustment of the interbank market.But, the network configuration also increases risk contagion among the interbank market.However, the relationship between the network configuration and the systemic risk is a disputable issue in the research area.On one hand, the systemic risk is a typical small probability event which only happens in extreme circumstance.On the other hand, simulated interbank market network in the research area is different from real interbank market configuration.So building a realistic network model and researching the behavior of real network model in extreme circumstance are the key issues.Based on reality interbank network model, two evaluation parameters of the systemic risk: VaR and ES are presented in this paper.Firstly, Monte Carlo method is utilized to simulate the external impact of interbank system.Then, the systemic risk VaR and ES which can reflect the small probability characters of the systemic risk are estimated and the tail properties of interbank system loss are captured.Secondly, real bank parameters are utilized to calibrate three kinds of interbank network in simulation.Such a method ensures the reality and reliability of simulation results.Finally, three valuable conclusions are drawn: (1) External impact will trigger contagion.The interbank system loss will change from norm distribution to heavy tail distribution and then to bimodal distribution.(2) The contagion probability of high density interbank network is smaller than that of low density network, but the destruction is much higher.(3) The potential contagion will enlarge the systemic risk and default contagion effect will increase exponentially.A model which can evaluate the extent of the destruction of the systemic risk in extreme condition is presented.Furthermore, the simulation results comprehensively reveal the relationship between the network configuration and the systemic risk.

Key words: systemic risk, interbank network, monte carlo simulation, VaR

中图分类号:

F830

隋聪, 谭照林, 王宗尧. 基于网络视角的银行业系统性风险度量方法[J]. 中国管理科学, 2016, 24(5): 54-64.

SUI Cong, TAN Zhao-lin, WANG Zong-yao. A Network Perspective Measurement Method for Banking Systemic Risk[J]. Chinese Journal of Management Science, 2016, 24(5): 54-64.

参考文献

[1] Iori G, Jafarey S, Padilla F.Systemic risk on the interbank market[J].Journal of Economic Behavior & Organization, 2006, 61(4):525-542.

[2] Allen F, Gale D.Financial contagion[J].Journal of Political Economy, 2000, 108(1):1-33.

[3] Nier E, Yang Jing, Yorulmazer T, et al.Network models and financial stability[J].Journal of Economic Dynamics and Control, 2007, 31(6):2033-2060.

[4] May R M, Arinaminpathy N.Systemic risk:The dynamics of model banking systems[J].Journal of the Royal Society Interface, 2010, 7(46):823-838.

[5] Gai P, Kapadia S.Contagion in financial networks[J]. Proceedings:Macthematical,Physical and Engineering Sciences,2010,466(2120):2401-2423.

[6] 隋聪, 迟国泰, 王宗尧.网络结构与银行系统性风险[J].管理科学学报, 2014,17(4):57-70.

[7] Wells S.UK interbank exposures:Systemic risk implications[J].Financial Stability Review, 2002, 13(12):175-182.

[8] Lublóy Á.Domino effect in the Hungarian interbank market[J].Hungarian Economic Review, 2005, 52(4):377-401.

[9] Mistrulli P.E.Assessing financial contagion in the interbank market:Maximum entropy versus observed interbank lending patterns[J].Journal of Banking & Finance, 2011, 35(5):1114-1127.

[10] 黄聪, 贾彦东.金融网络视角下的宏观审慎管理——基于银行间支付结算数据的实证分析[J].金融研究, 2010(4):1-14.

[11] 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(1):68-78.

[12] 陈庭强, 何建敏.基于复杂网络的信用风险传染模型研究[J].中国管理科学, 2014, 22(11):1-10.

[13] 欧阳红兵, 刘晓东.中国金融机构的系统重要性及系统性风险传染机制分析——基于复杂网络的视角[J].中国管理科学, 2015, 23(10):30-37.

[14] Boss M, Elsinger H, Summer M, et al.Network topology of the interbank market[J].Quantitative Finance, 2004, 4(6):677-684.

[15] Soramäki K, Bech M L, Arnold J, et al.The topology of interbank payment flows[J].Physica A, 2007, 379(1):317-333.

[16] Becher C, Millard S, Soramäki K.The network topology of CHAPS Sterling[R].Working Paper UK:Bank of England, 2008.

[17] Krause A, Giansante S.Interbank lending and the spread of bank failures:A network model of systemic risk[J].Journal of Economic Behavior and Organization, 2012, 83(3):583-608.

[18] Sheldon G, Maurer M.Interbank lending and systemic risk:an empirical analysis for Switzerland[J].Swiss Society of Economics and Statistics, 1998, 134(4):685-704.

[19] Eisenberg L, Noe T H.Systemic risk in financial systems[J].Management Science, 2001, 47(2):236-249.

[20] Barrat A, Barthelemy M, Pastor-Satorras R, et al.The architecture of complex weighted networks[J].Proceedings of the National Academy of Sciences, 2004, 101(11):3747-3752.

基于网络视角的银行业系统性风险度量方法

A Network Perspective Measurement Method for Banking Systemic Risk

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	吴文洋,蒋海,唐绅峰. 数字化转型、网络关联性与银行系统性风险[J]. 中国管理科学, 2024, 32(3): 9-19.
[2]	陈杰,邢灵博,李胃胜,陈志祥,陈崇萍. 不完备质量下带有风险厌恶的库存决策模型[J]. 中国管理科学, 2024, 32(2): 54-64.
[3]	王江涛,蔡雅,郑承利. 一类自适应的风险预测算法及其应用[J]. 中国管理科学, 2023, 31(8): 1-8.
[4]	赵静, 郭晔. 金融强监管、影子银行与银行系统性风险[J]. 中国管理科学, 2023, 31(7): 50-59.
[5]	王虎, 李守伟, 马瑜寅, 刘晓星. 基于共同持股网络的基金系统性风险研究[J]. 中国管理科学, 2023, 31(6): 82-90.
[6]	张鹏, 崔淑琳, 李璟欣. 一致性均值-CVaR可信性投资组合优化[J]. 中国管理科学, 2023, 31(6): 111-121.
[7]	刁姝杰, 匡海波, 朱佳钰, 孟斌. 基于混合CVaR的出口海陆仓质押率与物流服务努力水平联合决策研究[J]. 中国管理科学, 2023, 31(5): 93-103.
[8]	于辉, 李鑫. 零售商运营视角下投贷联动CVaR利率决策模型[J]. 中国管理科学, 2023, 31(2): 255-265.
[9]	叶五一,李艾琳,焦守坤. 基于动态模型平均的大豆期货市场风险溢出研究[J]. 中国管理科学, 2023, 31(12): 1-10.
[10]	欧阳资生, 杨希特, 黄颖. 嵌入网络舆情指数的中国金融机构系统性风险传染效应研究[J]. 中国管理科学, 2022, 30(4): 1-12.
[11]	刘志东, 张培元, 荆中博. 跨行业风险溢出冲击下我国银行业系统性风险研究[J]. 中国管理科学, 2022, 30(12): 1-12.
[12]	张飞鹏, 徐一雄, 邹胜轩, 陈艳. 基于LGCNET多层网络的中国A股上市公司系统性风险度量[J]. 中国管理科学, 2022, 30(12): 13-25.
[13]	张小婉, 易荣华, 俞莹, 王颖. 基于滚窗VAR的沪港通波动溢出效应测度[J]. 中国管理科学, 2022, 30(11): 42-51.
[14]	吴鑫育, 李心丹, 马超群. 基于期权与高频数据信息的VaR度量研究[J]. 中国管理科学, 2021, 29(8): 13-23.
[15]	高倩倩, 范宏. 基于银行-资产双边网络模型的系统性风险及投资策略研究[J]. 中国管理科学, 2021, 29(7): 1-12.