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The Multiplex Dependency Structure of Financial Markets

Musmeci, N; Nicosia, V; Aste, T; Di Matteo, T; Latora, V; (2017) The Multiplex Dependency Structure of Financial Markets. Complexity , 2017 , Article 9586064. 10.1155/2017/9586064. Green open access

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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.

Type: Article
Title: The Multiplex Dependency Structure of Financial Markets
Open access status: An open access version is available from UCL Discovery
DOI: 10.1155/2017/9586064
Publisher version: https://doi.org/10.1155/2017/9586064
Language: English
Additional information: Copyright © 2017 Nicolo Musmeci et al. This is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1502929
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