中国与其他主要新兴市场国家间股市极端风险的跨市场传染

doi:10.16381/j.cnki.issn1003-207x.2022.1770

Abstract

Abstract:

Within the framework of economic globalization， an intensifying interconnectedness among financial markets has been observed， particularly exacerbated by the aftermath of the 2008 global financial crisis. The phenomenon of financial risk contagion across geographical boundaries has garnered considerable scholarly and regulatory scrutiny on a global scale. Emerging markets have ascended as pivotal engines propelling global economic expansion， a big number of international investors has been focused the China’s financial markets， which have emerged as a focal point within the emerging markets sphere. Meanwhile， the other emerging markets will boost their external openness to improve the efficiency in terms of asset allocation. Against this contextual tapestry， empirical investigation into the cross-market transmission of extreme risks amongst China and other preeminent emerging equity markets serves a dual purpose： elucidating the mechanistic pathways of extreme risk propagation and furnishing vital theoretical underpinnings along with strategic directives aimed at forestalling and mitigating such perils， thereby safeguarding and nurturing the stability and robust， orderly progression of our nation's financial markets.The scholarly contribution of this paper is twofold　（1） It examines the interconnectedness between the Chinese and other principal emerging market equities from a tail-risk perspective， thereby augmenting the extant literature on mean and volatility spillovers across stock markets；（2） To ascertain the asymmetric influences of positive and negative market shocks on tail-risk transmission， the MVMQ-CAViaR model is adapted to incorporate asymmetry， formulating an Asymmetric MVMQ-CAViaR model. This refined approach facilitates an in-depth scrutiny of the cross-market contagion dynamics of extreme risks among China and other major emerging market equities. The significance of these extreme risk contagion effects is empirically assessed using constructed Wald tests， with quantile impulse response functions subsequently employed to unravel the underlying transmission mechanisms of extreme risk among equity markets.

Key words: emerging markets, asymmetrical MVMQ-CAViaR model, extreme risk spillover

CLC Number:

F830.9

Ke Yang, Xin Liu, Fengping Tian. Cross-Market Contagion of Stock Market's Extreme Risks between China and Other Major Emerging Market Countries[J]. Chinese Journal of Management Science, 2025, 33(7): 44-53.

Figures/Tables 6

References 28

[1]	McQueen G， Roley V V. Stock prices， news， and business conditions［J］. The Review of Financial Studies， 1993， 6（3）： 683-707.
[2]	Connolly R A， Wang F A. Economic news and stock market linkages： Evidence from the U S， U K， and Japan［J］.Proceeding of the Second Joint Central Bank Research Conference on Risk Management and Systemic Risk， 1998（1）： 211-240.
[3]	Stulz R M. A Model of international asset pricing［J］. Journal of Financial Economics， 1981，9（4）： 383-406.
[4]	周开国，杨海生，伍颖华. 中国香港股票市场的溢出效应和收益引导角色—基于亚太地区股票市场的分析［J］. 管理科学学报， 2018， 21（5）： 22-43.
	Zhou K G， Yang H S， Wu Y H. Spillover effects and the leading role of Hong Kong stock market： Analysis of Asian-Pacific stock markets［J］. Journal of Management Sciences in China， 2018，21（5）： 22-43.
[5]	张小婉，易荣华，俞莹，等.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.
	Zhang X W， Yi R H， Yu Y， et al. The measure of volatility spillover effect of Shanghai-Hong Kong stock connect based on rolling window VAR［J］. Chinese Journal of Management Science， 2022， 30（11）： 42-51.
[6]	Liu P， Huang W Q. Spatial analysis of sovereign risk from the perspective of EPU spillovers［J］. International Review of Economics & Finance， 2024， 89： 427-443.
[7]	Karolyi G A. A multivariate GARCH model of international transmissions of stock returns and volatility： The case of the United States and Canada［J］. Journal of Business & Economic Statistics， 1995， 13（1）： 11-25.
[8]	Boyson N M， Stahel C W， Stulz R M. Hedge fund contagion and liquidity shocks［J］.The Journal of Finance， 2010， 65（5）： 1789-1816.
[9]	王鹏，吴金宴. 基于协高阶矩视角的沪港股市风险传染分析［J］. 管理科学学报， 2018， 21（6）： 29-42.
	Wang P， Wu J Y. Risk contagion between Shanghai and Hong Kong stock market： A perspective of higher order co-moments［J］. Journal of Management Sciences in China， 2018， 21（6）： 29-42.
[10]	Hong Y， Qu B， Yang Z， et al. The contagion of fake news concern and extreme stock market risks during the COVID—19 period［J］. Finance Research Letters， 2023，58： 104258.
[11]	Gong J， Wang G J， Zhou Y， et al. Spreading of cross-market volatility information： Evidence from multiplex network analysis of volatility spillovers［J］. Journal of International Financial Markets， Institutions and Money， 2023， 83： 101733.
[12]	樊智，张世英. 多元GARCH建模及其在中国股市分析中的应用［J］. 管理科学学报， 2003，6（2）： 68-73.
	Fan Z， Zhang S Y. Multivariate GARCH modeling and its application in volatility analysis of Chinese stock markets［J］. Journal of Management Sciences in China， 2003， 6（2）： 68-73.
[13]	张碧琼. 中国股票市场信息国际化：基于EGARCH模型的检验［J］. 国际金融研究， 2005（5）： 68-73.
	Zhang B Q. Information internationalization in China’s stock markets： A EGARCH-based test［J］. Studies of International Finance， 2005（5）： 68-73.
[14]	Johansson A C， Ljungwall C. Spillover effects among the greater China stock markets［J］. World Development， 2009， 37（4）： 839-851.
[15]	淳伟德，付君实，赵如波. 基于混合Copula函数的金融市场非线性极端风险传染研究［J］. 预测， 2015， 34（4）： 53-58.
	Chun W D， Fu J S， Zhao R B. The study on the financial market nonlinear risk contagion based on mixed copula model［J］. Forecasting， 2015， 34（4）： 53-58.
[16]	Zhang W， He X， Hamori S. Volatility spillover and investment strategies among sustainability—related financial indexes： Evidence from the DCC—GARCH—based dynamic connectedness and DCC—GARCH—t—copula approach［J］. International Review of Financial Analysis， 2022， 83： 102223.
[17]	林宇. 中国股市与国际股市的极值风险传导效应研究［J］. 中国管理科学， 2008， 16（4）： 36-43.
	Lin Y. Study on contagion effect between Chinese stock market and other international stock markets［J］.Chinese Journal of Management Science， 2008， 16（4）： 36-43.
[18]	张瑞锋，张世英.基于VS—MSV模型的金融市场波动溢出分析及实证研究［J］.系统工程，2007，25（8）： 1-6.
	Zhang R F， Zhang S Y. Volatility spillover analysis and empirical study on the financial market based on VS-MSV model［J］. Systems Engineering，2007，25（8）： 1-6.
[19]	Oh D H， Patton A J. Modeling dependence in high dimensions with factor copulas［J］.Journal of Business & Economic Statistics， 2017， 35（1）： 139-154.
[20]	刘晓星，段斌，谢福座. 股票市场风险溢出效应研究：基于EVT-Copula-CoVaR模型的分析［J］.世界经济， 2011，34（11）： 145-159.
	Liu X X， Duan B， Xie F Z. Research on risk spillover effect in stock market： Analysis based on EVT-copula-CoVaR model［J］.The Journal of World Economy， 2011，34（11）： 145-159.
[21]	周孝华，陈九生. 基于Copula-ASV-EVT-CoVaR模型的中小板与创业板风险溢出度量研究［J］. 系统工程理论与实践， 2016， 36（3）： 559-568.
	Zhou X H， Chen J S. Study on the risk spillover effect between the small and medium-sized board market and the second board market in China based on Copula-ASV-EVT-CoVaR model［J］. Systems Engineering—Theory & Practice， 2016， 36（3）： 559-568.
[22]	彭选华. 基于DCC-Copula-SV-M-t模型的股市系统性风险溢出分析［J］. 数理统计与管理， 2019， 38（5）： 929-939.
	Peng X H. Systematic risk spillover analysis of stock market based on DCC-copula-SV-M-t model［J］. Journal of Applied Statistics and Management， 2019， 38（5）： 929-939.
[23]	Jiang C， Li Y， Xu Q， et al. Measuring risk spillovers from multiple developed stock markets to China： A vine-copula-GARCH-MIDAS model［J］. International Review of Economics & Finance， 2021， 75： 386-398.
[24]	吴金宴，王鹏. 哪些因素影响了股市风险传染？——来自行业数据的证据［J］. 中国管理科学， 2022， 30（8）： 57-68.
	Wu J Y， Wang P. Factors affecting the risk contagion of the stock market： An evidence from industry-level data［J］.Chinese Journal of Management Science， 2022， 30（8）： 57-68.
[25]	王克达，庞晓波，王姗姗. 金融危机对全球股票市场的传染研究：基于复杂网络分析方法［J］. 世界经济研究， 2018（4）： 28-39+135.
	Wang K D， Pang X B， Wang S S. The contagion of financial crisis to global stock market based on complex network approach［J］. World Economy Studies， 2018（4）： 28-39+135.
[26]	刘海云，吕龙. 全球股票市场系统性风险溢出研究——基于ΔCoVaR和社会网络方法的分析［J］. 国际金融研究， 2018（6）： 22-33.
	Liu H Y， Lü L. Research on the spillover of systemic risk in the global stock markets—Analysis based on and social network［J］. Studies of International Finance， 2018（6）： 22-33.
[27]	Engle R F， Manganelli S. CAViaR： Conditional autoregressive value at risk by regression quantiles［J］. Journal of Business & Economic Statistics， 2004， 22（4）： 367-381.
[28]	White H， Kim T H， Manganelli S. VAR for VaR： Measuring tail dependence using multivariate regression quantiles［J］. Journal of Econometrics， 2015， 187（1）：169-188.

国家（地区）名称	均值	最大值	最小值	标准差	偏度	峰度	JB检验	ADF值
中国	0.028	9.233	-9.950	1.717	-0.274	7.027	0.001	0.001
印度尼西亚	0.056	7.623	-11.306	1.307	-0.693	11.979	0.001	0.001
新加坡	0.016	8.866	-10.628	1.072	-0.285	12.985	0.001	0.001
巴西	0.041	13.678	-18.749	1.718	-0.292	11.955	0.001	0.001
南非	0.043	41.341	-40.980	1.513	-0.173	287.534	0.001	0.001

国家（地区）名称	对称性模型拟合结果		非对称性模型拟合结果
国家（地区）名称	市场1	市场2	市场1	市场2
印度尼西亚	0.98	1.08	1.06	1.08
新加坡	0.98	1.03	1.01	1.01
巴西	1.03	0.93	1.01	0.98
南非	1.01	1.03	0.95	0.93

国家（地区）名称	对称性模型的DQ检验		非对称性模型的DQ检验
国家（地区）名称	市场1	市场2	市场1	市场2
印度尼西亚	0.343	0.026	0.163	0.042
新加坡	0.639	0.161	0.158	0.320
巴西	0.336	0.235	0.379	0.300
南非	0.294	0.150	0.663	0.846

国家（地区）名称	c₁	a₁₁	a₁₂	d₁₁	d₁₂	b₁₁	b₁₂
国家（地区）名称	c₂	a₂₁	a₂₂	d₂₁	d₂₂	b₂₁	b₂₂
印度尼西亚	-0.045	-0.165^***	-0.012	0.100^***	0.210^**	0.984^***	-0.069^***
	(0.028)	(0.051)	(0.038)	(0.032)	(0.085)	(0.008)	(0.024)
	-0.229^***	-0.079	-0.052	0.050	0.909^***	0.040^**	0.728^***
	(0.084)	(0.111)	(0.082)	(0.079)	(0.181)	(0.020)	(0.047)
新加坡	-0.044^*	-0.109^***	-0.079^*	0.092^**	0.229^***	0.990^***	-0.096^***
	(0.024)	(0.028)	(0.047)	(0.038)	(0.079)	(0.008)	(0.029)
	-0.069^**	0.012	-0.105^**	0.039	0.380^***	0.013	0.870^***
	(0.028)	(0.038)	(0.050)	(0.036)	(0.087)	(0.010)	(0.027)
巴西	-0.062^***	-0.150^***	0.078^***	0.037	0.126^***	0.985^***	-0.033^***
	(0.020)	(0.034)	(0.024)	(0.026)	(0.038)	(0.005)	(0.011)
	-0.169^***	-0.047	0.005	0.046	0.303^***	0.008	0.893^***
	(0.060)	(0.047)	(0.053)	(0.060)	(0.098)	(0.013)	(0.034)
南非	0.018	-0.174^***	0.086^***	0.083^**	0.061	0.969^***	0.004
	(0.032)	(0.048)	(0.026)	(0.033)	(0.058)	(0.009)	(0.025)
	-0.710^***	-0.105	-0.944^***	0.213	0.818^***	0.109^*	0.338^***
	(0.223)	(0.139)	(0.027)	(0.153)	(0.318)	(0.056)	(0.103)

国家（地区）名称	H₀：a₁₂=a₂₁=d₁₂=d₂₁=b₁₂=b₂₁=0		H₀：a₁₂=d₁₂=b₁₂=0		H₀：a₂₁=d₂₁=b₂₁=0
国家（地区）名称	Wald值	接受或拒绝原假设	Wald值	接受或拒绝原假设	Wald值	接受或拒绝原假设
印度尼西亚	0.02	拒绝	0.03	拒绝	0.04	拒绝
新加坡	0.00	拒绝	0.01	拒绝	0.10	拒绝
巴西	0.00	拒绝	0.00	拒绝	0.40	接受
南非	0.00	拒绝	0.00	拒绝	0.02	拒绝

Cross-Market Contagion of Stock Market's Extreme Risks between China and Other Major Emerging Market Countries

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