The Multiplex Dependency Structure of Financial Markets

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 t...

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Main Authors: Nicolò Musmeci, Vincenzo Nicosia, Tomaso Aste, Tiziana Di Matteo, Vito Latora
Format: Article
Language:English
Published: Wiley 2017-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2017/9586064
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author Nicolò Musmeci
Vincenzo Nicosia
Tomaso Aste
Tiziana Di Matteo
Vito Latora
author_facet Nicolò Musmeci
Vincenzo Nicosia
Tomaso Aste
Tiziana Di Matteo
Vito Latora
author_sort Nicolò Musmeci
collection DOAJ
description 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.
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institution Kabale University
issn 1076-2787
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language English
publishDate 2017-01-01
publisher Wiley
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series Complexity
spelling doaj-art-0bc72526010f4b938dbfdbd7d82594c82025-02-03T06:12:28ZengWileyComplexity1076-27871099-05262017-01-01201710.1155/2017/95860649586064The Multiplex Dependency Structure of Financial MarketsNicolò Musmeci0Vincenzo Nicosia1Tomaso Aste2Tiziana Di Matteo3Vito Latora4Department of Mathematics, King’s College London, The Strand, London WC2R 2LS, UKSchool of Mathematical Sciences, Queen Mary University of London, Mile End Road, London E1 4NS, UKDepartment of Computer Science, University College London, Gower Street, London WC1E 6BT, UKDepartment of Mathematics, King’s College London, The Strand, London WC2R 2LS, UKSchool of Mathematical Sciences, Queen Mary University of London, Mile End Road, London E1 4NS, UKWe 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.http://dx.doi.org/10.1155/2017/9586064
spellingShingle Nicolò Musmeci
Vincenzo Nicosia
Tomaso Aste
Tiziana Di Matteo
Vito Latora
The Multiplex Dependency Structure of Financial Markets
Complexity
title The Multiplex Dependency Structure of Financial Markets
title_full The Multiplex Dependency Structure of Financial Markets
title_fullStr The Multiplex Dependency Structure of Financial Markets
title_full_unstemmed The Multiplex Dependency Structure of Financial Markets
title_short The Multiplex Dependency Structure of Financial Markets
title_sort multiplex dependency structure of financial markets
url http://dx.doi.org/10.1155/2017/9586064
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