基于随机矩阵理论的股市网络拓扑性质研究

doi:10.12005/orms.2018.0022

运筹与管理 ›› 2018, Vol. 27 ›› Issue (1): 144-152.DOI: 10.12005/orms.2018.0022

基于随机矩阵理论的股市网络拓扑性质研究

谢赤^1,2, 胡珏¹, 王钢金^1,2

1.湖南大学工商管理学院,湖南长沙 410082;
2.湖南大学金融与投资管理研究中心,湖南长沙 410082

收稿日期:2015-08-20 出版日期:2018-01-25
作者简介:谢赤(1963-),男,湖南株洲人,博士,教授,博士生导师,研究方向为金融工程与风险管理。
基金资助:
国家自然科学基金项目(71373072);国家自然科学基金项目(71340014);高等学校博士点专项科研基金项目(20130161110031)

Study on Topological Property of Stock Market’s Network Based on Random Matric Method

XIE Chi^1,2, HU Jue¹, WANG Gang-jin^1,2

1.College of Business Administration, Hunan University, Changsha 410082, China;
2.Center of Finance and Investment Management, Hunan University, Changsha 410082, China

Received:2015-08-20 Online:2018-01-25

摘要/Abstract

摘要： 本文运用随机矩阵理论(RMT)和相关系数动态演化模型建立全球股指二次“去噪”相关系数矩阵,并采用阀值法构建全球股市网络,进而分析该网络拓扑结构特性和解释风险在网络中的传染效应。研究发现,全球股市网络呈现出“小世界”效应;在θ=0.1数量水平下,全球股市网络具有较强的鲁棒性。同时,英国和荷兰的股票市场风险传染对网络整体的冲击较大;股市网络中各个股市间的风险传染路径与相关国家经济实力相关联,体现出较强的同配性。

关键词: 股市网络, 拓扑结构, 风险传染, 随机矩阵理论

Abstract: In this paper, we apply the random matric theory(RMT)and dynamic model of correlation coefficient to establish a worldwide stock markets’ correlation coefficient matric of twice of denoising. We use the threshold method to build the stock markets’ network, and further analyze the topological properties of the network and explain the risk contagion among stock markets in the network. The study indicates that, the network is a small-world network. Under the threshold θ=0.1, the worldwide stock markets network is a strongly robust. As for risk contagion, the stock markets of England and Nederland have a strong impact on the stock market network than others. Besides, the risk contagion among stock markets in the network shows an assortativety.

Key words: stock markets network, topological structure, risk contagion, random matric theory

中图分类号:

F830.9

谢赤, 胡珏, 王钢金. 基于随机矩阵理论的股市网络拓扑性质研究[J]. 运筹与管理, 2018, 27(1): 144-152.

XIE Chi, HU Jue, WANG Gang-jin. Study on Topological Property of Stock Market’s Network Based on Random Matric Method[J]. Operations Research and Management Science, 2018, 27(1): 144-152.

参考文献

[1] Baba N, Packer F. From turmoil to crisis: dislocations in the FX swap market before and after the failure of Lehman Brothers[J]. Journal of International Money and Finance, 2009, 28(8): 1350-1374.
[2] Aizenman J, Pasricha G K, Determinants of financial stress and recovering during the great recession[J]. International Journal of Financial Economic, 2012, 17(4): 347-372.
[3] Eichengreen B, Mody A, Nedoljkovic M, Sarno L, How the subprime crisis went global: evidence from bank credit default swap spreads[J]. Journal of International Money and Finance, 2012, 31(5): 1299-1318.
[4] Liu L. Inerrnational stock market interdependence: are developing markets the same as developed markets[J]. Journal of Internatinal Finance Markets, Institution and Money, 2013, 26(3): 226-238
[5] Engsted T, Tanggaard C. The comovement of US and UK stock market[J]. European Financial Management, 2004, 10(4): 593-607
[6] Sohnke M B, Stephen J, Taylor, Yawhuei W. The euro and european financial market dependence[J]. Journal of Banking & Finance, 2007, 31(5): 1461-1481
[7] 赵胜民,谢晓闻,方意.中国在全球股市风险传染网络中的角色研究——基于次贷危机和欧债危机时期的样本分析[J].财经论丛,2013,(5):57-65
[8] Nobi A, Maeng S E, Ha G G, Jae W L. Effects of global financial crisis on network structure in a local stock market[J]. Physica A, 2014, 407: 135-143
[9] 韩华,吴翎燕,宋宁宁.基于随机矩阵的金融网络模型. 物理学报,2014,63(13):1-10.
[10] Watts D, Strogatz S. Collective dynamics of small-world networks[J]. Nature, 1998, 393(6684): 440-442.
[11] Barabasi A L, Albert R. Emergence of scaling in random networks[J]. Science, 1999, 286(5439): 509-512.
[12] 马洪伟,周溪召.随机交通网络渐近连同可靠性分析[J]. 运筹与管理,2015,24(6):45-50.
[13] 马军,董琼,杨德礼.基于风险管理的动态供应链超网络均衡模型[J].运筹与管理,2015,24(1):1-8.
[14] Namaki A, Shirazi A H, Raei R, Jafari G R. Network analysis of a financial market based on genuine correlation and threshold method[J]. Physica A, 2011, 390(21): 3835-3841.
[15] 左正强.股票市场与债券市场之间风险外溢效应研究[J]. 财经纵横,2014,10:155-158.
[16] Chengyi Tu. Cointegration-based financail networks study in Chinese stock market[J]. Physica A, 2014, 402: 244-254.
[17] 安静,安海忠,杨广林.基于复杂网络的矿业上市公司股权融资的金融机构关系分析[J]. 资源与产业,2014,16(2):124-127.
[18] Kwapień J, Drod S, Os′wiecimka P. The bulk of the stock market correlation matrix is not pure noise, Physica A. 2006, 359: 589-606.
[19] Dongming S, Tumminello M, Zhou W X, Mantegna R. Evolution of worldwide stock markets, correlation structure, and correlation based graphs[J]. Physical Review E, 2011, 84(2): 026108.
[20] Xiaodan Z, Xiangdong L, Yuan Z, Cheng L. Chaotic dynamic behavior analysis and control for a financial risk system[J]. Chinese Pysical B, 2013, 22(3): 260-265.
[21] Bonanno G, Vandewallc N, Mantegna R N. Taxonomy of stock market indices[J]. Physical Review E, 2000, 62(R): 7615-76I8.
[22] Ng A. Volatility spillover effects from japan and the US to the pacific-basin[J]. Journal of International Money and Finance, 2000, 19(2): 207-233.
[23] Sakthivel P. Correlation and volatility transmission across international stock markets: a bivariate GARCH analysis[J]. International Journal of Economics and Finance, 2012, 4(3): 253-264.
[24] Chiang T C, Jeon B N, Huimin L. Dynamic correlation analysis of financial contagion: evidence from asian markets[J]. Journal of International Money and Finance, 2007, 26(2): 1206-1228.
[25] Forbes K J, Rigobon R. No contagion, only interdependence: measuring stock market comovements[J]. The Journal of Finance, 2002, 57(5): 2223-2261.
[26] Laloux L, Cizeau P, Bouchaud J P, et al.. Noise dressing of financial correlation matrices[J]. Physical Review Letters, 1999, 83(7): 1468-1470.
[27] Plerou V, Gopikrishnan P, Rosenow B, et al.. Random matrix approach to cross correlations in financial data[J]. Physical Review E, 2002, 65(6): 066126, 1-18.
[28] Sharifi S, Crane M, Shamaie A, et al.. Random matrix theory for portfolio optimization: a stability approach[J]. Physical A: Statistical and Theoretical Physics, 2004, 335: 629-643.
[29] Kenett D Y, Shapira Y O, Jacob E. RMT Assessments of the market latent information embedded in the stocks’ raw, normalized and partial correlations[J]. Journal of Probability and Statisties, 2009, 2009(2009): 249370
[30] Tummincllo M, Astc T, Di Matteo T. A tool for filtering information in complex systems[J]. Proceeding National Academy of Science of the United States of America, 2005, 102(30): 10421-10426.
[31] 任小叶.基于复杂网络的金融市场建模方法研究[D].中国科学技术大学,2013.

基于随机矩阵理论的股市网络拓扑性质研究

Study on Topological Property of Stock Market’s Network Based on Random Matric Method

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