Research on the risk spillovers between Shanghai, Shenzhen and Hong Kong stock markets—Based on the time varying ΔCoVaR model
LIN Juan1,2,3, ZHAO Hailong1
1. Department of Finance, School of Economics, Xiamen University, Xiamen 361005, China; 2. Key Laboratory of Econometrics(Xiamen University), Ministry of Education, Xiamen 361005, China; 3. Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen 361005, China
Abstract：This paper uses the ΔCoVaR approach to estimate the tail risk spillover effects between Shanghai/Shenzhen and Hong Kong stock markets from November 2006 to December 2018. The results show that:1) There exist the significantly positive spillover effects between Shanghai/Shenzhen and Hong Kong stock markets in both directions; 2) The spillover effects from Hong Kong to Shanghai/Shenzhen stock market are stronger than those from Shanghai/Shenzhen to Hong Kong stock market; 3) There exist more fluctuations in the spillover effects between Shenzhen and Hong Kong stock markets than those in the spillover effects between Shanghai and Hong Kong stock markets; 4) After the launch of the ‘Shanghai-Hong Kong Stock Connect’ and ‘Shenzhen-Hong Kong Stock Connect’, the spillover effects between Hong Kong and Shanghai/Shenzhen stock markets do not appear to change significantly.
林娟, 赵海龙. 沪深股市和香港股市的风险溢出效应研究——基于时变ΔCoVaR模型的分析[J]. 系统工程理论与实践, 2020, 40(6): 1533-1544.
LIN Juan, ZHAO Hailong. Research on the risk spillovers between Shanghai, Shenzhen and Hong Kong stock markets—Based on the time varying ΔCoVaR model. Systems Engineering - Theory & Practice, 2020, 40(6): 1533-1544.
[1] Hamao Y, Masulis R, Ng V. Correlations in price changes and volatility across international stock markets[J]. The Review of Financial Studies, 1990, 3(2):281-307. [2] Diebold F X, Yilmaz K. Measuring financial asset return and volatility spillovers, with application to global equity markets[J]. The Economic Journal, 2009, 119(534):158-171. [3] Diebold F X, Yilmaz K. Better to give than to receive:Predictive directional measurement of volatility spillovers[J]. International Journal of Forecasting, 2012, 28(1):57-66. [4] Wang Y, Pan Z, Wu C. Volatility spillover from the US to international stock markets:A heterogeneous volatility spillover GARCH model[J]. Journal of forecasting, 2018, 37:385-400. [5] 张兵,范致镇,李心丹. 中美股票市场的联动性研究[J]. 经济研究, 2010(11):141-151. Zhang B, Fan Z Z, Li X D. Comovement between China and US's stock markets[J]. Economic Research Journal, 2010(11):141-151. [6] 郑挺国, 刘堂勇. 股市波动溢出效应及其影响因素分析[J]. 经济学季刊, 2018, 17(2):669-692. Zheng T G, Liu T Y. Volatility spillovers across stock markets and its influencing factors[J]. China Economic Quarterly, 2018, 17(2):669-692. [7] 洪永淼, 成思危, 刘艳辉, 等. 中国股市与世界其他股市之间的大风险溢出效应[J]. 经济学(季刊), 2004, 3(3):703-726. Hong Y M, Cheng S W, Liu Y H, et al. Extreme risk spillover between Chinese stock market and international stock markets[J]. China Economic Quarterly, 2004, 3(3):703-726. [8] 李红权, 洪永淼, 汪寿阳. 我国A股市场与美股、港股的互动关系研究:基于信息溢出视角[J]. 经济研究, 2011(8):15-25. Li H Q, Hong Y M, Wang S Y. Information spillover among China's A-shares market, US stock market and HK stock market[J]. Economic Research Journal, 2011(8):15-25. [9] Longin F M. From value at risk to stress testing:The extreme value approach[J]. Journal of Banking and Finance, 2000, 24:1097-1130. [10] Hong Y, Liu Y, Wang S. Granger causality in risk and detection of extreme risk spillover between financial markets[J]. Journal of Econometrics, 2009, 150(2):271-287. [11] White H, Kim T, Manganelli S. VaR for VaR:Measuring tail dependence using multivariate regression quantiles[J]. Journal of Econometrics, 2015, 187(1):169-188. [12] Adrian T, Brunnermeier M. CoVaR[J]. The American Economic Review, 2016, 106(7):1705-1741. [13] López-Espinosa G, Moreno A, Rubia A, et al. Short-term wholesale funding and systemic risk:A global CoVaR approach[J]. Journal of Banking & Finance, 2012, 36(12):3150-3162. [14] 杨子晖, 陈雨恬, 谢锐楷. 我国金融机构系统性金融风险度量与跨部门风险溢出效应研究[J]. 金融研究, 2018(10):19-37. Yang Z H, Chen Y T, Xie R K. Research on systemic risk measures and cross-sector risk spillover effect of financial institutions in China[J]. Journal of Financial Research, 2018(10):19-37. [15] 罗辉,张朝晖. 内地股票市场与香港股票市场间风险溢出分析[J]. 宏观经济研究, 2013(9):113-118. Luo H, Zhang C H. Analysis on the risk spillover effect between the mainland stock markets and Hong Kong stock market[J]. Research on Macroeconomics, 2013(9):113-118. [16] Reboredo J C, Rivera-Castro M A, Ugolini A. Downside and upside risk spillover between exchange rates and shock prices[J]. Journal of Banking and Finance, 2016, 62:76-96. [17] Jin X Y. Downside and upside risk spillovers from China to Asian stock markets:A CoVaR-copula approach[J]. Financial Research Letters, 2018, 25:202-212. [18] 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(11):145-159. Liu X X, Duan B, Xie F Z. Research on the risk spillover effect between stock markets:Based on the EVT-Copula-CoVaR model[J]. The Journal of World Economy, 2011(11):145-159. [19] 周爱民, 韩菲. 股票市场和外汇市场间风险溢出效应研究——基于GARCH-时变Copula-CoVaR模型的分析[J]. 国际金融研究, 2017(11):54-64. Zhou A M, Han F. Research on the risk spillover effect between stock and exchange rate markets-Based on the GARCH-time varying Copula-CoVaR model[J]. Studies of International Finance, 2017(11):54-64. [20] 陈九生, 周孝华. 基于单因子MSV-CoVaR模型的金融市场风险溢出度量研究[J]. 中国管理科学, 2017(1):21-26. Chen J S, Zhou X H. Research on the risk spillover effect of China's financial market based on a factor-MSV-CoVaR model[J]. Chinese Journal of Management Science, 2017(1):21-26. [21] 刘海云, 吕龙. 全球股票市场系统性风险溢出研究——基于Δ CoVaR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 systemic risk spillover effect between the global stock markets-Based on the Δ CoVaR and social network models[J]. Studies of International Finance, 2018(6):22-33. [22] Girardi G, Ergün A T. Systemic risk measurement:Multivariate GARCH estimation of CoVaR[J]. Journal of Banking and Finance, 2013, 37(8):3169-3180. [23] Reboredo J C, Ugolini A. Systemic risk in European sovereign debt markets:A CoVaR-copula approach[J]. Journal of International Money and Finance, 2015, 51:214-244. [24] Oh D H, Patton A J. Time-varying systemic risk:Evidence from a dynamic copula model of CDS spreads[J]. Journal of Business & Economic Statistics, 2018, 36(2):181-195. [25] 李政, 涂晓枫, 卜林. 金融机构系统性风险:重要性与脆弱性[J]. 财经研究, 2019, 45(2):100-112. Li Z, Tu X F, Bu L. Systemic risks of financial institutions:Importance and vulnerability[J]. Journal of Finance and Economics, 2019, 45(2):100-112. [26] 张兵, 封思贤, 李心丹, 等. 汇率与股价变动关系:基于汇改后数据的实证研究[J]. 经济研究, 2008(9):70-81. Zhang B, Feng S X, Li X D, et al. Exchange rates and stock prices interactions in China:An empirical studies after 2005 exchange rate reform[J]. Economic Research Journal, 2008(9):70-81. [27] 张明, 陈骁, 魏伟, 等. 上下坡路是同一条路消费升降级均与房地产有关[EB/OL]. 金融界. (2019-01-07). http://opinion.jrj.com.cn/2019/01/07083126864442.shtml [28] 巴曙松, 覃川桃, 朱元倩. 中国股票市场与房地产市场的联动关系[J].系统工程, 2009(9):22-28. Ba S S, Qin C T, Zhu Y Q. Interaction between China real estate and stock market[J]. Systems Engineering, 2009(9):22-28.
2020, Vol. 40 
