The Multiplex Dependency Structure of Financial Markets

Nicolò Musmeci,Vito Latora,Tiziana Di Matteo,Vincenzo Nicosia,Tomaso Aste

doi:10.1155/2017/9586064

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.

Highlights

In the last decade, network theory has been extensively applied to the analysis of financial markets
Financial markets and complex systems in general are comprised of many interacting elements, and understanding their dependency structure and its evolution with time is essential to capture the collective behaviour of these systems, to identify the emergence of critical states, and to mitigate systemic risk arising from the simultaneous movement of several factors
The reason for this choice is to provide a complete picture of the market dependency structure: Pearson layer accounts for linear dependency, Kendall layer for monotonic nonlinearity, and Tail dependency for correlation in the tails of returns distribution while Partial correlation detects direct assetasset relationships which are not explained by the market

Summary

Introduction

Network theory has been extensively applied to the analysis of financial markets. A multiplex network approach, which considers the multilayer structure of a system in a consistent way, is a natural and powerful way to take into account simultaneously several distinct kinds of dependency. In this work we exploit the power of a multiplex approach to analyse simultaneously different kinds of dependencies among financial time series. The theory of multiplex network is a recently introduced framework that allows describing real-world complex systems consisting of units connected by relationships of different kinds as networks with many layers, where the links at each layer represent a different type of interaction between the same set of nodes [13, 14]. We extend the multiplex approach to financial market time series, with the purpose of analysing the role of different measures of dependencies, namely, the Pearson, Kendall, Tail, and Partial correlation. The obtained planar graph corresponding to Partial correlations has been converted into an undirected graph and included in the multiplex

Results

Industrials

Discussion

Materials and Methods

Conflicts of Interest