Paper Search

Complex Systems and Complexity Science >

2020 , Vol. 17 >Issue 2: 54 - 66

DOI: https://doi.org/10.13306/j.1672-3813.2020.02.007

On the Risk Contagion Effect of International Stock Market Based on 15 Stock Markets Data from 2007 to 2018

  • LIU Chao ,
  • WANG Shujiao ,
  • LIU Chenqi ,
  • LIU Siyuan ,
  • LIU Chao ,
  • WANG Shujiao ,
  • LIU Chenqi ,
  • LIU Siyuan
Expand
  • 1. College of Economics and Management, Beijing University of Technology, Beijing 100124, China;
    2. Research Base of Beijing Modern Manufacturing Development, Beijing 100124, China;
    3. Department of Computer Science, University of Southern California, Los Angeles 90001, USA;
    4. Dublin Institute, Beijing University of Technology, Beijing 100124, China

Online published: 2020-06-24

Fold

Abstract

This paper uses the AR (1)-GJR (1, 1)-SKT model to describe the marginal distribution of 15 stock index returns. A hybrid R-Vine Copula model is constructed to analyze the risk contagion effect of international stock markets under the four crisis events, which include the subprime mortgage crisis, the European debt crisis, the abnormal fluctuations of the Chinese stock market in 2015 and the Sino-US trade friction in 2018. The empirical results show that the international stock markets maintain symmetrical top-to-bottom dependence characteristics in the long term. The risk contagion will cause the Kendall rank correlation coefficient and tail correlation coefficient among stock markets to rise suddenly. The subprime crisis has a strong contagious effect and a long duration, and the European debt crisis is relatively mild. In 2015, the abnormal fluctuations in the Chinese stock market had a strong contagious effect on international stock markets, but the duration was short. In 2018, the Sino-US trade friction held a weaker contagious effect on the international stock markets. China's Shanghai and Shenzhen stock markets are more integrated with Hong Kong stock market in China.

Key words： dependency; risk contagion; financial crisis; mixed R-vine copula

Cite this article

LIU Chao , WANG Shujiao , LIU Chenqi , LIU Siyuan , LIU Chao , WANG Shujiao , LIU Chenqi , LIU Siyuan . On the Risk Contagion Effect of International Stock Market Based on 15 Stock Markets Data from 2007 to 2018[J]. Complex Systems and Complexity Science, 2020 , 17(2) : 54 -66 . DOI: 10.13306/j.1672-3813.2020.02.007

References

[1]Hui C H, Lo C F, Lau C S. Option-implied correlation between iTraxx Europe Financials and non-financials indexes: a measure of spillover effect in european debt crisis[J]. Journal of Banking & Finance, 2013, 37(9): 36943703.
[2]吴良, 燕鑫, 杨宇程. 流动性危机与中国股灾之谜[J]. 统计研究, 2017, 34(12): 8798.
Wu Liang, Yan Xin, Yang Yucheng. Liquidity crisis and china’s stock market crash[J]. Statistical Research, 2017, 34(12): 8798.
[3]Cao W. The β Coefficient and chow stability test of chinese foreign trade stocks under the sino-US trade war[C].International Conference on Intelligent and Interactive Systems and Applications. Springer, Cham, 2019: 230237.
[4]郭文伟, 陈妍玲. 沪、深、港股市相依状态转换及其危机传染效应研究[J]. 管理评论, 2017, 29(12):316.
Guo W W, Chen Y L. An empirical research on the conversion of dependency state and crisis contagion among shanghai, shenzhen and hong kong stock markets[J]. Management Review, 2017, 29(12):316.
[5]徐晓萍, 杨欢, 韩定定. 金融危机对中国证券网络影响的实证研究[J]. 复杂系统与复杂性科学, 2012,9(2):1726.
Xu Xiaoping, Yang Huan, Han Dingding. Empirical study on chinese stock network under financial crisis[J] Complex Systems and Complexity Science, 2012,9(2):1726.
[6]韩冬梅, 王雯. 复杂网络视角下的国际证券市场结构特征分析[J]. 复杂系统与复杂性科学, 2014, 11(3):5057.
Han Dongmei, Wang Wen. Analysis of international stock market structure and feature from the perspective of complex network[J] Complex Systems and Complexity Science, 2014, 11(3):5057.
[7]Wang K, Chen Y H, Huang S W . The dynamic dependence between the chinese market and other international stock markets: A time-varying copula approach[J]. International Review of Economics and Finance, 2011, 20(4):654664.
[8]Brechmann E C, Czado C. Risk management with high-dimensional vine copulas:An analysis of the euro stoxx 50[J]. Statistics & Risk Modeling, 2013, 30(4):307342.
[9]Allen D E, Ashraf M A, Mcaleer M, et al. Financial dependence analysis: applications of vine copulas[J]. Statistica Neerlandica, 2013, 67(4):403435.
[10] Brechmann E, Czado C, Paterlini S. Flexible dependence modeling of operational risk losses and its impact on total capital requirements[J]. Journal of Banking & Finance, 2014, 40(1):271285.
[11] Koliai L. Extreme risk modeling: an EVT-pair-copulas approach for financial stress tests[J]. Journal of Banking & Finance, 2016, 70:122.
[12] Aloui R, Assa M S B. Relationship between oil, stock prices and exchange rates: A vine copula based GARCH method[J]. North American Journal of Economics & Finance, 2016, 37:458471.
[13] Hussain S I, Li S . The dependence structure between chinese and other major stock markets using extreme values and copulas[J]. International Review of Economics & Finance, 2018, 56: 421437.
[14] Zhang D, Yan M, Tsopanakis A. Financial stress relationships among Euro area countries: an R-vine copula approach[J]. Social Science Electronic Publishing, 2018(2):122.
[15] Maya R A L, Gomez-Gonzalez J E, Velandia L F M. Latin american exchange rate dependencies: a regular vine copula approach[J]. Contemporary Economic Policy, 2015, 33(3):535549.
[16] 叶五一, 李潇颖, 缪柏其. 基于藤Copula方法的持续期自相依结构估计及预测[J]. 中国管理科学, 2015, 23(11):2938.
Ye Wuyi, Li Xiaoying, Miao Baiqi. Auto-dependence structure estimating and forecasting of duration based on vine copula[J]. Chinese Journal of Management Science, 2015, 23(11):2938.
[17] 吴吉林, 陈刚, 黄辰. 中国A、B、H股市间尾部相依性的趋势研究——基于多机制平滑转换混合Copula模型的实证分析[J]. 管理科学学报, 2015, 18(2):5065.
Wu J L, Chen G, Huang C. Long-term dynamic trends in tail dependence of Chinese A,B and H Stock Markets: Empirical analysis based on multi-regime smoothing transition mixed Copula model[J]. Journal of Management Sciences in China, 2015, 18(2): 5065.
[18] 孙红梅, 朱伟琪, 崔百胜. 金融危机的传导效应——以欧洲金融危机为例[J]. 金融论坛, 2018(1):1626.
Sun Hongmei, Zhu Weiqi, Cui Baisheng. The transmission effect of financial crisis-the sample of european financial crisis[J]. Finance Forum, 2018(1):1626.
[19] 杨坤, 于文华, 魏宇. 基于R-vine copula的原油市场极端风险动态测度研究[J]. 中国管理科学, 2017, 25(8):1929.
Yang Kun, Yu Wenhua, Wei Yu. Dynamic measurement of extreme risk among various crude oil markets based on R-vine copula[J]. Chinese Journal of Management Science, 2017, 25(8):1929.
[20] 陈九生, 周孝华. 沪港通背景下沪港股市联动性研究[J]. 北京理工大学学报(社会科学版), 2017(2):8793.
Chen Jiusheng, Zhou Xiaohua. Research on the Co-movement relationships between Shanghai and HongKong stock markets under the background of “Shanghai-Hong Kong stock connect program”[J]. Journal of Beijing Institute of Technology (Social Science Edition), 2017(2):8793.
[21] 范国斌, 曾勇, 黄文光. 一种多资产组合风险度量解决之道:正则藤Copula[J]. 数量经济技术经济研究, 2013, 30(1):88102.
Fan Guobin, Zeng Yong, Huang Wenguang. A new approach for measuring the risk of portfolios with multiple assets[J]. The Journal of Quantitative Technical Economics, 2013, 30(1):88102.
[22] 张保帅, 彭小兵. 投资组合极值风险测度——基于T-Copula-SV-T-EVT模型[J]. 北京理工大学学报(社会科学版), 2014, 16(5):7781.
Zhang Baoshuai, Peng Xiaobing. Risk measurement of financial portfolio based on T-copula-SV-T-EVT model [J]. Journal of Beijing Institute of Technology (Social Science Edition), 2014, 16(5):7781.
[23] 张帮正, 魏宇, 余江, 等. 基于EVT-Vine-copula的多市场相关性及投资组合选择研究[J]. 管理科学, 2014(3):133144.
Zhang Bangzheng, Wei Yu, Yu Jiang, et al. The study of correlation and portfolio selection among multi-markets based on EVT-vine-copula [J]. Journal of Management Science, 2014(3):133144.
[24] 李强, 周孝华, 李婧. 基于Copula-ASV-EVT的QFII和HS300指数相关性风险度量[J]. 系统工程理论与实践, 2017(3): 570579.
Li Qiang, Zhou Xiaohua, Li Jing.Measuring the dependence risk between QFII and HS300 index based on the Copula-ASV-EVT model[J].Systems Engineering Theory Practice, 2017, 37(3): 570579.
[25] Mensi W, Hammoudeh S, Reboredo J C, et al. Are Sharia stocks, gold and U.S. Treasury hedges and/or safe havens for the oil-based GCC markets?[J]. Emerging Markets Review, 2015, 24:101121.
[26] Acar E F, Genest C, Nelehová J. Beyond simplified pair-copula constructions[J]. Journal of Multivariate Analysis, 2012, 110(5):7490.
[27] Wei G N F, Scheffer M. Mixture air-copula-constructions[J]. Journal of Banking & Finance, 2015, 54:175191.
[28] Bedford T, Cooke R M. Probability density decomposition for conditionally dependent random variables modeled by vines.[J]. Annals of Mathematics & Artificial Intelligence, 2001, 32(14):245268.
[29] 张国富, 皇甫星, 杜子平. 基于R藤的人民币汇率相依结构参数估计不确定性分析[J]. 世界经济研究, 2015(12):2434.
Zhang Guofu, Huang Fuxing, Du Ziping. Uncertainty analysis of structural parameter estimation based on R vine RMB exchange rate[J]. World Economy Study, 2015(12): 2434.
[30] Pourkhanali A, Kim J M, Tafakori L, et al. Measuring systemic risk using vine-copula[J]. Economic Modelling, 2016, 53:6374.
[31] 马锋, 魏宇, 黄登仕. 基于vine copula方法的股市组合动态VaR测度及预测模型研究[J]. 系统工程理论与实践, 2015, 35(1):2636.
Ma Feng, Wei Yu, Huang Dengshi. Measurement of dynamic stocks portfolio VAR and its forecasting model based on vine copula [J].Systems Engineering Theory Practice, 2015, 35(1): 2636.
[32] Kurowicka D, Cooke R M. Completion problem with partial correlation vines[J]. Linear Algebra & Its Applications, 2006, 418(1):188200.
[33] Brechmann E C, Czado C, Kurowicka D. Selecting and estimating regular vine copulae and application to financial returns[J]. Computational Statistics & Data Analysis, 2013, 59(1):5269.
[34] 郭文伟. 国内外股市相依结构演化及其危机传染效应研究[J]. 国际金融研究, 2016, 354(10):6373.
Guo Wenwei. Research on the evolution of domestic and foreign stock market dependent structure and its crisis contagion effect[J]. Studies of International Finance, 2016, 354(10): 6373.
Options
Outlines

/

〈 〉

Copyright © 2017 Editorial Office of Complex Systems and Complexity Science
Tel.：0532-85953597 E-mail:fzkxbjb@qdu.edu.cn