Volatility and Cross Correlation Across Major Stock Markets
Volatility and Cross Correlation Across Major Stock Markets
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DOI：
10.2139/ssrn.57948
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发表时间：
1997-12
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通讯作者：
Latha Ramchand;Raul Susmel
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文献类型：
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作者：
Latha Ramchand;Raul Susmel
几篇论文已经证明了这样一个事实，即当市场波动性更大时，主要股票市场的相关性更高-这是通过比较子时期的无条件相关性或使用随时间变化的条件相关性来实现的。在本文中，我们研究的相关性和方差之间的关系，在一个有条件的时间和状态变化的框架。我们使用一种开关磁阻（SWITCHING）技术，它做两件事。首先，它使我们能够将方差建模为状态变化。第二，二元SWITCH模型允许我们从条件方差到状态变化的协方差和相关性，因此可以测试不同方差体系之间的相关性差异。我们发现，与低方差状态相比，当美国市场处于高方差状态时，美国与其他世界市场之间的相关性平均高出2至3.5倍。我们还发现，相比于Gestival框架，我们的SWestival模型产生的投资组合选择导致更高的夏普比率。
Several papers have documented the fact that correlations across major stock markets are higher when markets are more volatile - this is done by comparing unconditional correlations over sub-periods or by using conditional correlations that are time varying. In this paper we examine the relation between correlation and variance in a conditional time and state varying framework. We use a switching ARCH (SWARCH) technique that does two things. One, it enables us to model variance as state varying. Two, a bivariate SWARCH model allows us to go from conditional variance to state varying covariances and correlations and hence test for differences in correlations across variance regimes. We find that the correlations between the U.S. and other world markets are on average 2 to 3.5 times higher when the U.S. market is in a high variance state as compared to a low variance regime. We also find that, compared to a GARCH framework, the portfolio choices resulting from our SWARCH model lead to higher Sharpe ratios.