Dynamical complexity analysis of multivariate financial data

Characterization of joint dynamics of multivariate financial time series calls for the analysis based on joint intrinsic temporal and information-theoretic scales. Yet a rigorous account of dynamical complexity of such time series is hampered by the univariate natures and mathematical artefacts associated with the existing methods. To that end, we employ multi-variate multiscale entropy (MMSE) in order to associate multivariate complexity with long-range correlations, direct and indirect couplings, and synchronies among the data channels. Simulations on major stock indices support the approach.