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首页> 外文期刊>The quarterly review of economics and finance >On the globalization of stock markets: An application of Vector Error Correction Model, Mutual Information and Singular Spectrum Analysis to the G7 countries
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On the globalization of stock markets: An application of Vector Error Correction Model, Mutual Information and Singular Spectrum Analysis to the G7 countries

机译：关于股票市场全球化：矢量误差校正模型，互信息和奇异谱分析在G7国家中的应用

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摘要

This paper analyzes stock market relationships among the G7 countries between 1973 and 2009 using three different approaches: (i) a linear approach based on cointegration, Vector Error Correction (VECM) and Granger Causality; (ii) a nonlinear approach based on Mutual Information and the Global Correlation Coefficient; and (iii) a nonlinear approach based on Singular Spectrum Analysis (SSA). While the cointegration tests are based on regression models and capture linearities in the data, Mutual Information and Singular Spectrum Analysis capture nonlinear relationships in a non-parametric way. The framework of this paper is based on the notion of market integration and uses stock market correlations and linkages both in price levels and returns. The main results show that significant co-movements occur among most of the G7 countries over the period analyzed and that Mutual Information and the Global Correlation Coefficient actually seem to provide more information about the market relationships than the Vector Error Correction Model and Granger Causality. However, unlike the latter, the direction of causality is difficult to distinguish in Mutual Information and the Global Correlation Coefficient. In this respect, the nonlinear Singular Spectrum Analysis technique displays several advantages, since it enabled us to capture nonlinear causality in both directions, while Granger Causality only captures causality in a linear way. The results also show that stock markets are closely linked both in terms of price levels and returns (as well as lagged returns) over the 36 years analyzed.
机译：本文使用三种不同的方法分析了七国集团在1973年至2009年之间的股票市场关系：（i）基于协整，矢量误差校正（VECM）和格兰杰因果关系的线性方法； （ii）基于互信息和全局相关系数的非线性方法； （iii）基于奇异频谱分析（SSA）的非线性方法。协整测试基于回归模型并捕获数据中的线性度，而互信息和奇异频谱分析则以非参数方式捕获非线性关系。本文的框架基于市场整合的概念，并在价格水平和回报上使用了股票市场的相关性和联系。主要结果表明，在所分析的时期内，大多数七国集团成员国之间都发生了重大的共同变动，并且相互信息和全球相关系数实际上似乎比矢量误差校正模型和格兰杰因果关系提供了更多有关市场关系的信息。但是，与后者不同，因果关系的方向很难在互信息和全局相关系数中区分。在这方面，非线性奇异频谱分析技术显示了多个优势，因为它使我们能够捕获两个方向的非线性因果关系，而格兰杰因果关系仅以线性方式捕获因果关系。结果还显示，在所分析的36年中，股票市场在价格水平和收益（以及滞后收益）方面紧密相关。

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