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基于滚窗VAR的沪港通波动溢出效应测度 被引量:4

The Measure of Volatility Spillover Effect of Shanghai-Hong Kong Stock Connect Based on Rolling Window VAR
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摘要 股市间的互联互通是把“双刃剑”,在加强联动性的同时也扩大了风险敞口。作为中国股市对外开放的重大举措,沪港通开通六年来的效果如何是业界关注的议题。本文运用滚窗VAR方法计算沪港通前后各1000个交易日期间上海、香港和纽约股市的时变波动溢出指数,进而分析两两市场之间波动溢出效应的大小、传导方向变化。研究发现,沪港通的实施,增大了沪市与香港股市的波动溢出效应,与美国的波动溢出效应相对减小,沪港通在加强沪、港两市联动性的同时弱化了与纽约股市的联动性;改变了沪港两市之间波动溢出效应的传导方向,由单一的港→沪溢出变为以沪市为主的双向沪←→港溢出,同时也增大了对纽约股市的溢出效应,增强了沪市的竞争力。这一结果符合经济基础假说和协同市场假说。本文的研究改进了股市间互联互通波动溢出效应程度和传导方向的测度方法,并为中国沪港通政策效果评价及进一步的国际板开设提供决策参考依据。 The interconnection between the stock markets is a “double-edged sword”,which not only strengthens linkage but also expands risk exposure.As a major measure of China’s stock market opening to the outside world,the effect of the Shanghai-Hong Kong Stock Connect in the past six years is a topic of concern to the industry.Whether the implementation of the Shanghai-Hong Kong Stock Connect has improved the competitiveness of the mainland stock market,and whether it has increased the volatility spillover effect between the Shanghai and Hong Kong stock markets,these are the themes of this paper.In the context of the opening of the Shanghai-Hong Kong Stock Connect,in order to explore the impact and changes of the Shanghai and Hong Kong markets,the volatility spillover index is measured to observe the effect of the stock market.Furthermore,the stock market volatility spillover index is measured under time-varying conditions,and the volatility spillover effect and the change of risk transmission direction before and after the opening of Shanghai-Hong Kong Stock Connect are analyzed.At the same time,in order to analyze the volatility spillover effect with the international market,the New York stock market is added as a research sample to examine the linkage between the Shanghai and Hong Kong stock markets and the international stock market.The rolling window VAR model is adopted,the constant coefficient VAR model within the window is estimated and the corresponding volatility spillover index is calculated by fixing the sample window width,and then the window period by period is moved to estimate the time-varying volatility spillover index.The data include Shanghai Composite Index,Hang Seng Index and S&P 500 Index.The time span is 1,000 trading days before and after the opening day of Shanghai-Hong Kong Stock Connect.It is found that the implementation of the Shanghai-Hong Kong Stock Connect has increased the volatility spillover effect of the Shanghai and Hong Kong stock markets,and has relatively reduced the volatility spillover effect of the U.S.The Shanghai-Hong Kong Stock Connect has strengthened the linkage between the Shanghai and Hong Kong stock markets while weakening its relationship with New York.The transmission direction of the volatility spillover effect between Shanghai and Hong Kong is changed,from a single Hong Kong→Shanghai spillover to a two-way Shanghai←→Hong Kong spillover dominated by the Shanghai stock market.At the same time,the impact on the New York stock market is also increased and the competitiveness of Shanghai stock market is enhanced.This result is consistent with the economic foundation hypothesis and the synergy market synergy hypothesis.The research in this paper quantitatively measures the degree and direction of the volatility spillover effect between the Shanghai and Hong Kong stock markets of interconnection,provides an evaluation of the effect of China’s Shanghai-Hong Kong Stock Connect policy,and further provides a reference for decision-making for the establishment of the International Board.
作者 张小婉 易荣华 俞莹 王颖 ZHANG Xiao-wan;YI Rong-hua;YU Ying;WANG Ying(College of Economics and Management,China Jiliang University,Hangzhou 310018,China;College of Modern Science and Technology,China Jiliang University,Hangzhou 310018,China;Faculty of Business and Law,Anglia Ruskin University,East Road,Cambridge CB11PT,UK)
出处 《中国管理科学》 CSSCI CSCD 北大核心 2022年第11期42-51,共10页 Chinese Journal of Management Science
基金 国家自然科学基金资助项目(71871205)。
关键词 沪港通 滚窗VAR 时变波动溢出 波动溢出指数 Shanghai-Hong Kong Stock Connect rolling window VAR time-varying volatility spillover volatility spillover index
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