Dynamically Measuring Statistical Dependencies in Multivariate Financial Time Series Using Independent Component Analysis

Abstract

We present a computationally tractable approach to dynamically measure statistical dependencies in multivariate non-Gaussian signals. The approach makes use of extensions of independent component analysis to calculate information coupling, as a proxy measure for mutual information, between multiple signals and can be used to estimate uncertainty associated with the information coupling measure in a straightforward way. We empirically validate relative accuracy of the information coupling measure using a set of synthetic data examples and showcase practical utility of using the measure when analysing multivariate financial time series.