Table of Contents
ISRN Signal Processing
Volume 2013, Article ID 434832, 14 pages
http://dx.doi.org/10.1155/2013/434832
Research Article

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

Machine Learning Research Group, Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK

Received 30 March 2013; Accepted 4 May 2013

Academic Editors: A. Krzyzak, C.-M. Kuo, S. Kwong, W. Liu, and F. Perez-Cruz

Copyright © 2013 Nauman Shah and Stephen J. Roberts. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.