多层网络视角下金融机构关联性的演化特征研究

doi:10.16381/j.cnki.issn1003-207x.2019.0002

中国管理科学 ›› 2020, Vol. 28 ›› Issue (12): 35-43.doi: 10.16381/j.cnki.issn1003-207x.2019.0002

多层网络视角下金融机构关联性的演化特征研究

李守伟^1,2, 文世航³, 王磊¹, 何建敏¹, 龚晨¹

1. 东南大学经济管理学院, 江苏南京 211189;
2. 东南大学金融复杂性与风险管理研究中心, 江苏南京 211189;
3. 中国人民大学财政金融学院, 北京 100872

收稿日期:2019-01-02 修回日期:2019-03-20 出版日期:2020-12-20 发布日期:2021-01-11
通讯作者: 李守伟(1984-),男(汉族),安徽蚌埠人,东南大学经济管理学院,教授,博士,研究方向:金融复杂性与风险管理,E-mail:lishouwei@seu.edu.cn. E-mail:lishouwei@seu.edu.cn
基金资助:
国家自然科学基金资助项目（71671037，71673043，71971055）；江苏高校哲学社会科学研究重点项目（2018SJZDI046）；国家社会科学基金资助重大专项研究项目（18VSJ035）；江苏省第十六批"六大人才高峰"高层次人才项目（JY-004）；中央高校基本科研业务费专项资金资助（2242019K41043）

Evolution Characteristics of Financial Institutions' Interrelationships from the Perspective of Multilayer Network

LI Shou-wei^1,2, WEN Shi-hang³, WANG Lei¹, HE Jian-min¹, GONG Chen¹

1. School of Economics and Management, Southeast University, Nanjing 211189, China;
2. Research Center for Financial Complexity and Risk Management, Southeast University, Nanjing 211189, China;
3. School of Finance, Renmin University of China, Beijing 100872, China

Received:2019-01-02 Revised:2019-03-20 Online:2020-12-20 Published:2021-01-11

摘要/Abstract

摘要： 基于股票收益率间Pearson相关性、Kendall秩相关性以及Tail相关性，构建了金融机构多层网络模型，其中三种相关性分别对应多层网络中Pearson层、Kendall层和Tail层。根据2010年10月至2018年3月期间我国上市金融机构的数据，实证分析了金融机构多层网络结构演化特征。实证分析结果表明：Kendall层和Tail层中的平均权重均高于Pearson层的平均权重，Pearson层和Kendall层中的平均权重变化趋势比较相似，但前者波动幅度明显大于后者；Pearson层与Kendall层的边独特性变化趋势非常相似，而Tail层的与它们有较大的差别；任意两层的度相关性均为正相关，但随时间波动均较为剧烈；Pearson层与Kendall层的相似性整体上较高，Tail层与Pearson层和Kendall层的相似性整体上均较低；多层网络中平均权重、边独特性、度相关性和相似性等结构指标变化趋势与股票市场行情相关。

关键词: 多层网络, 金融机构, 关联性, 网络结构

Abstract: The linear correlation between stock returns can be described by Pearson correlation, but the very important non-linear characteristics of stock markets and the correlation between stock returns when extreme events occur are not measured by Pearson correlation. However, correlation information between stock returns that Pearson correlation cannot measure can be provided by Kendall rank correlation and Tail correlation. Therefore, in order to analyze the dependence structure of stocks, it is very important to adopt different correlation measures to characterize the relationship between stock returns. Therefore, based on Pearson correlation, Kendall rank correlation and Tail correlation of stock returns, the evolution characteristics of multilayer network structures of financial institutions in China is empirically studied by this paper.
Firstly, the method of building the multilayer network model of financial institutions is given, which consists of two steps. One is to calculate Pearson, Kendall and Tail correlation of stock returns. The other is to filter the correlation between stocks by using the minimum spanning tree method. Secondly, based on the above model and the financial stock data from October 2010 to March 2018 in China, the evolution characteristics of multilayer network structures of financial institutions is analyzed through the structural indicators, such as average weight, edge uniqueness, degree correlation and similarity in the multilayer network.
Empirical results show that:the average weights of the Kendall and Tail layers are higher than those of the Pearson layer; the trends of average weights of the Pearson and Kendall layers are similar, but the fluctuation amplitude of the former is obviously larger than that of the latter; the trends of edge uniqueness of the Pearson layer and the Kendall layer are very similar, but that of the Tail layer is quite different from them; the degree correlation of any two layers is positive, but it fluctuates sharply with time; the Pearson layer and the Kendall layer have higher similarity, the similarity between the Tail layer and the Pearson layer and the Kendall layer is lower overall; the structural indicators such as average weight, edge uniqueness, degree correlation and similarity in the multilayer network are related to the stock market quotation.From the perspective of the multilayer network theory, the evolutionary characteristics of multilayer correlation among financial institutions in China and its internal relationship with the stock market quotation are explored by this paper, which enriches the study of the multilayer network theory in the financial field. And the relevant research results have a certain practical significance for maintaining the stability of financial markets.

Key words: multilayer network, financial institutions, interrelationship, network structure

中图分类号:

F832

李守伟, 文世航, 王磊, 何建敏, 龚晨. 多层网络视角下金融机构关联性的演化特征研究[J]. 中国管理科学, 2020, 28(12): 35-43.

LI Shou-wei, WEN Shi-hang, WANG Lei, HE Jian-min, GONG Chen. Evolution Characteristics of Financial Institutions' Interrelationships from the Perspective of Multilayer Network[J]. Chinese Journal of Management Science, 2020, 28(12): 35-43.

参考文献

[1] Acemoglu D, Ozdaglar A, Tahbaz-Salehi A. Systemic risk and stability in financial networks[J]. American Economic Review, 2015, 105(2):564-608.
[2] Capponi A, Chen P C. Systemic risk mitigation in financial networks[J]. Journal of Economic Dynamics and Control, 2015, 58:152-166.
[3] Poledna S, Thurner S. Elimination of systemic risk in financial networks by means of a systemic risk transaction tax[J]. Quantitative Finance, 2016, 16(10):1599-1613.
[4] 高国华,潘英丽.基于资产负债表关联的银行系统性风险研究[J].管理工程学报,2012,26(4):162-168.
[5] 李守伟,何建敏.不同网络结构下银行间传染风险研究[J].管理工程学报,2012,26(4):71-76.
[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] 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.
[8] 王占浩,郭菊娥,薛勇.资产负债表关联、价格关联与银行间风险传染[J].管理工程学报, 2016, 30(2):202-209.
[9] 刘超,徐君慧,周文文.中国金融市场的风险溢出效应研究——基于溢出指数和复杂网络方法[J].系统工程理论与实践,2017,37(4):831-842.
[10] 杨海军,胡敏文.基于核心-边缘网络的中国银行风险传染[J].管理科学学报,2017,20(10):44-56.
[11] Gałązka M. Characteristics of the Polish stock market correlations[J]. International Review of Financial Analysis, 2011, 20(1):1-5.
[12] 韩华,刘婉璐,汪金水.证券市场的动态网络模型构建与演化规律研究[J].管理学报,2013, 10(2):299-304.
[13] Nobi A, Maeng S E, Ha G G, et al. Effects of global financial crisis on network structure in a local stock market[J]. Physica A Statistical Mechanics and Its Applications, 2014, 407(C):135-143.
[14] 秦春雷,张巍,朱艳春.金融危机下证券市场网络结构演化的实证分析[J].商业研究,2015,57(3):98-103.
[15] Majapa M, Gossel S J. Topology of the south african stock market network across the 2008 financial crisis[J]. Physica A Statistical Mechanics and Its Applications, 2016, 445:35-47.
[16] 张骥,龙海明.基于系统性风险背景的证券市场网络动态演化[J].求索,2017,(4):110-116.
[17] 邵华明,马永谈,朱涛.股票市场稳定性测度及其作用机制——基于复杂网络模型视角的分析[J].财经科学,2017,(5):54-66.
[18] 刘海飞,柏巍,李冬昕,等.沪港通交易制度能提升中国股票市场稳定性吗?——基于复杂网络的视角[J].管理科学学报,2018,21(1):97-110.
[19] Buldyrev S V, Parshani R, Paul G, et al. Catastrophic cascade of failures in interdependent networks.[J]. Nature, 2010, 464(7291):1025-1028.
[20] Gao Jianxi, Li Daqing, Havlin S. From a single network to a network of networks[J]. National Science Review, 2014, 1(3):346-356.
[21] Boccaletti S, Bianconi G, Criado R, et al. The structure and dynamics of multilayer networks[J]. Physics Reports, 2014, 544(1):1-122.
[22] Brummitt C D, Kobayashi T. Cascades in multiplex financial networks with debts of different seniority[J]. Physical Review E Statistical Nonlinear and Soft Matter Physics, 2015, 91(6):062813.
[23] Bargigli L, Iasio D G, Infante L, et al. The multiplex structure of interbank networks[J]. Quantitative Finance, 2015, 15(4):673-691.
[24] Peralta G, Crisóstomo R. Financial contagion with spillover effects:A multiplex network approach[R]. Working Paper, European Systemic Risk Board, 2016.
[25] Aldasoro I, Alves I. Multiplex interbank networks and systemic importance:an application to European data[J]. Journal of Financial Stability, 2018, 35:17-37.
[26] Poledna S, Molina-Borboa J L, Martínez-Jaramillo S, et al. The multi-layer network nature of systemic risk and its implications for the costs of financial crises[J]. Journal of Financial Stability, 2015, 20:70-81.
[27] Musmeci N, Nicosia V, Aste T, et al. The multiplex dependency structure of financial markets[J]. Complexity, 2017,DOI:10.1155/2017/9586064.
[28] Li Shouwei, Wen Shihang. Multiplex networks of the guarantee market:Evidence from China[J]. Complexity, 2017, DOI:10.1155/2017/9781890.
[29] Chen Wei, Jiang Manrui, Jiang Cheng. Constructing a multilayer network for stock market[J]. Soft Computing, 2019:1-17.
[30] 陈蓉,陈妙琼.金融资产的动态非线性相关:一个新的模型[J].管理评论,2012,24(2):12-23.
[31] Cherubini U, Luciano E, Vecchiato W. Copula methods in finance[M]. New Jersey:John Wiley & Sons, 2004.
[32] Mantegna R N. Hierarchical structure in financial markets[J]. The European Physical Journal B-Condensed Matter and Complex Systems, 1999, 11(1):193-197.
[33] 谢赤,边慧东,王纲金.牛熊市视角下股票关联网络动态拓扑结构研究——以上证50指数为例[J]. 复杂系统与复杂性科学,2017,14(1):66-74.
[34] Battiston F, Nicosia V, Latora V. Structural measures for multiplex networks[J]. Physical Review E Statistical Nonlinear & Soft Matter Physics, 2014, 89(3):032804.
[35] Nicosia V, Latora V. Measuring and modeling correlations in multiplex networks.[J]. Physical Review E Statistical Nonlinear & Soft Matter Physics, 2015, 92(3):032805.
[36] Battiston F, Nicosia V, Latora V. The new challenges of multiplex networks:Measures and models[J]. European Physical Journal Special Topics, 2017, 226(3):401-416.

多层网络视角下金融机构关联性的演化特征研究

Evolution Characteristics of Financial Institutions' Interrelationships from the Perspective of Multilayer Network

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