On the Globalization Stock Markets: An Application of VECM, Mutual Imformation and SSA to the G7

Resumo/Abstract: This paper analyzes the process of stock market globalization on the basis of three different approaches: (i) a linear approach based on cointegration, Vector Error Correction 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. While the cointegration tests are based on regression models and typically capture linearities in the data, Mutual Information and Singular Spectrum Analysis are well suited for capturing global non-parametric relationships in the data without imposing any structure or restriction on the model. The theoretical background is rooted on the concepts of strong and weak market integration, which are defined on the basis of the extent of causality that occurs in price transmission when the process is proportional or not over time. The data used in our empirical analysis were drawn from DataStream and comprise the natural logarithms of relative stock market indexes since 1973 for the G7 countries. The main results point to the conclusion that significant causal effects occur in this context and that Mutual Information and the Global Correlation Coefficient actually provide more information on this process than the Vector Error Correction Model, but the direction of causality is difficult to distinguish in the former case. In this respect, Singular Spectrum Analysis shows some advantages, since it enabled us to capture the nonlinear causality in both directions. There is evidence that stock markets are closely related both in the long-run and in the short-run over the 36 years analyzed and, in this sense, one may say that markets are quasi-globalized.

Outros seminários / Other seminars: Programa completo / Full programme.