Table of Contents Author Guidelines Submit a Manuscript
Complexity
Volume 2017 (2017), Article ID 9586064, 13 pages
https://doi.org/10.1155/2017/9586064
Research Article

The Multiplex Dependency Structure of Financial Markets

1Department of Mathematics, King’s College London, The Strand, London WC2R 2LS, UK
2School of Mathematical Sciences, Queen Mary University of London, Mile End Road, London E1 4NS, UK
3Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK
4Systemic Risk Centre, London School of Economics and Political Sciences, London WC2A 2AE, UK
5Dipartimento di Fisica ed Astronomia, Università di Catania and INFN, 95123 Catania, Italy

Correspondence should be addressed to Vito Latora; ku.ca.lumq@arotal.v

Received 25 May 2017; Accepted 16 July 2017; Published 20 September 2017

Academic Editor: Tommaso Gili

Copyright © 2017 Nicolò Musmeci et al. 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 propose here a multiplex network approach to investigate simultaneously different types of dependency in complex datasets. In particular, we consider multiplex networks made of four layers corresponding, respectively, to linear, nonlinear, tail, and partial correlations among a set of financial time series. We construct the sparse graph on each layer using a standard network filtering procedure, and we then analyse the structural properties of the obtained multiplex networks. The study of the time evolution of the multiplex constructed from financial data uncovers important changes in intrinsically multiplex properties of the network, and such changes are associated with periods of financial stress. We observe that some features are unique to the multiplex structure and would not be visible otherwise by the separate analysis of the single-layer networks corresponding to each dependency measure.