The multiplex dependency structure of financial markets

TL;DR

This paper introduces a multiplex network framework to analyze various dependency types in financial markets, revealing structural changes during financial stress periods that are not observable through single-layer analysis.

Contribution

It develops a novel multiplex network approach combining multiple dependency measures to study financial data, highlighting features unique to the multiplex structure.

Findings

Structural properties of multiplex networks change during financial stress periods

Multiplex analysis uncovers features not visible in single-layer networks

Different dependency types reveal distinct network dynamics

Abstract

We propose here a multiplex network approach to investigate simultaneously different types of dependency in complex data sets. In particular, we consider multiplex networks made of four layers corresponding respectively to linear, non-linear, 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.