Dynamically Measuring Statistical Dependencies in Multivariate Financial Time Series Using Independent Component Analysis. (2nd June 2013)

• Record Type: Journal Article
• Authors: Shah, Nauman; Roberts, Stephen J.
• Other Names: Krzyzak A. Academic Editor.; Kuo C.-M. Academic Editor.; Kwong S. Academic Editor.; Liu W. Academic Editor.; Perez-Cruz F. Academic Editor.
• Is Part Of: ISRN signal processing. Volume 2013(2013)
• Publication Date: 2013-06-02
• Subjects: Signal processing -- Periodicals; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2013/434832
• Languages: English
• ISSNs: 2090-5041

Abstract

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.