本文通过建立包含马尔科夫机制转换结构的MS-MHAR-DCC模型，并选取世界上比较发达的国家和地区股票市场的高频日内交易数据为样本，对多个股票市场波动相关性进行研究。通过引入包含马尔科夫结构的外部随机矩阵，本文识别出金融市场波动相关的截断时期，正态分布设定下相比在t分布设定下识别的截断时期更多且持续时间更长。在模型的截断时期内，多个股票市场的波动相关结构主要受到正向冲击，即在截断时期内的波动相关性大于平常状态的波动相关性。本文还发现，相同地域的股票市场间的动态波动相关性在大部分时期内表现为较强的正相关；美国股票市场和其余5个国家股票市场波动的动态相关性在大部分时期都表现为较强的正相关，表明美国作为全球巨头在世界金融市场波动的引导作用。
The financial globalization facilitates the flow of capital and good across the countries, and thus improves the operational efficiency of the financial markets. At the same time, the financial globalization enhances the risk transmission and thus leads to high volatility of financial markets.Therefore, the co-movement of financial markets is an important issue for risk managements, asset pricing and portfolio management.
A MHAR-DCC model with a Markov regime switching structure is developed to investigate the volatility co-movement of stock markets by employingthe high-frequency data from the six major stock markets in the world from 1 January 2010 to 31 December 2013.
The structural changes in co-volatility are identifed by introducing an exogenous stochastic component that follows a Markov regime switching process. There are more and longer-duration break periods identified by the model with the normal distribution as compared to that with the student-t distribution. In addition, the positive impact that scales up the covariance dominates the breakdown periods.There exhibit significantly positive correlations between the stock market within the same geographyregions as well as significantly positive correlations between the US stock market and the other markets, indicating the leading position of the US stock market in the co-movement of world stock markets.
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