Abstract
In this work we study multiple related (multivariate) time series from worldwide markets. We search for signs of coherence and/or synchronization using the main index as representative of the whole market. In order to better understand the relations between the time series we use two different techniques, entropy and variance-covariance matrices. We apply each procedure in a time dependent way to better understand the underlying dynamics of the system. We found that both methods show that world markets, regardless of their maturity status (mature or emergent), are behaving more and more alike over the last years. The simultaneous use of correlation and entropy to study multivariate time series is a promising approach in the sense that they capture different aspects of the collective system dynamics.
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Acknowledgements
One of authors (JAOM) would like to thank ESF (European Science Foundation) for COST action P10-STSM 00421, that made possible a visit to Dublin City University where part of this work was initiated.
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Matos, J.A.O., Gama, S.M.A., Ruskin, H.J., Sharkasi, A.A., Crane, M. (2011). A Study of Correlation and Entropy for Multiple Time Series. In: Machado, J., Luo, A., Barbosa, R., Silva, M., Figueiredo, L. (eds) Nonlinear Science and Complexity. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9884-9_29
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DOI: https://doi.org/10.1007/978-90-481-9884-9_29
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