The Multiplex Dependency Structure of Financial Markets

Musmeci, Nicoló; Nicosia, Vincenzo; Aste, Tomaso; Matteo, Tiziana Di; Latora, Vito

doi:10.1155/2017/9586064

Cited by 68 publications

(44 citation statements)

References 46 publications

(86 reference statements)

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“…A sparse precision matrix provides an easily interpretable and intuitive structure of the market state with all the most relevant dependencies directly interconnected in a sparse network. Furthermore, sparsity reduces the number of parameters from order n 2 (with n the number of variables) to order n preventing overfitting (Lauritzen 1996) and filtering out noisy correlations (Barfuss et al 2016, Musmeci et al 2017.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Market States

Procacci

Aste

2018

SSRN Journal

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We propose a novel methodology to define, analyse and forecast market states. In our approach market states are identified by a reference sparse precision matrix and a vector of expectation values. In our procedure each multivariate observation is associated to a given market state accordingly to a minimisation of a penalized Mahalanobis distance. The procedure is made computationally very efficient and can be used with a large number of assets. We demonstrate that this procedure is successfull at clustering different states of the markets in an unsupervised manner. In particular, we describe an experiment with one hundred log-returns and two states in which the methodology automatically associates states prevalently to pre-and post-crisis periods with one state gathering periods with average positive returns and the other state periods with average negative returns, therefore discovering spontaneously the common classification of 'bull' and 'bear' markets. In another experiment, with again one hundred log-returns and two states, we demonstrate that this procedure can be efficiently used to forecast off-sample future market states with significant prediction accuracy. This methodology opens the way to a range of applications in risk management and trading strategies in the context where the correlation structure plays a central role.

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Section: Introductionmentioning

confidence: 99%

Forecasting Market States

Procacci

Aste

2018

SSRN Journal

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“…The multilayer formulation allows for novel approaches to the study of the dynamics of the economy. Musmeci et al [83] applied the multilayer approach to study the structure of financial markets by building a multilayer network where each layer represents the same data but the links are constructed using different correlation measures: Pearson, Kendall, Tail and Partial correlation. In this way they can provide a complete picture of the market dependency structure as the Pearson layer accounts for linear dependencies, the Kendall layer for monotonic nonlinearity, the Tail gives information about correlations in the tails of the distributions and finally the Partial correlation detects direct relationship that are not explained by the market.…”

Section: Multilayer Network In Economymentioning

confidence: 99%

Multilayer Networks in a Nutshell

Aleta

Moreno

2019

Annu. Rev. Condens. Matter Phys.

176

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Complex systems are characterized by many interacting units that give rise to emergent behavior. A particularly advantageous way to study these systems is through the analysis of the networks that encode the interactions among the system's constituents. During the last two decades, network science has provided many insights in natural, social, biological and technological systems. However, real systems are more often than not interconnected, with many interdependencies that are not properly captured by single layer networks. To account for this source of complexity, a more general framework, in which different networks evolve or interact with each other, is needed. These are known as multilayer networks. Here we provide an overview of the basic methodology used to describe multilayer systems as well as of some representative dynamical processes that take place on top of them. We round off the review with a summary of several applications in diverse fields of science.

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“…The study of financial multiplex networks has only appeared recently. Empirical analyses of the financial multiplex networks of Colombia, UK, Mexico, Italy, Europe, and USA are provided by León et al [28], Langfield et al [29], Molina-Borboa et al [30], Bargigli et al [31], Aldasoro and Alves [32], and Musmeci et al [33], respectively.…”

Section: Introductionmentioning

confidence: 99%

“…Aldasoro and Alves [32] adopt data on interbank exposures broken down by both maturity and instrument type to investigate structures of the multiplex network of large European banks and find that the network presents positive correlated multiplexity and a high similarity between layers. Musmeci et al [33] analyze structural properties of the multiplex network of US stock markets, which includes four layers corresponding, respectively, to linear, nonlinear, tail, and partial correlations among a set of financial time series. They find that some features are unique to the multiplex structure and would not be visible otherwise by the separate analysis of the single-layer networks.…”

Section: Introductionmentioning

confidence: 99%

Multiplex Networks of the Guarantee Market: Evidence from China

Wen

2017

Complexity

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We investigate a multiplex network of the guarantee market with three layers corresponding to different types of guarantee relationships in China. We find that three single-layer networks all have the scale-free property and are of disassortative nature. A single-layer network is not quite representative of another single-layer network. The result of the betweenness centrality shows that central companies in one layer are not necessarily central in another layer. And the eigenvector centrality has the same result of the betweenness centrality except the top central company.

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The Multiplex Dependency Structure of Financial Markets

Cited by 68 publications

References 46 publications

Forecasting Market States

Forecasting Market States

Multilayer Networks in a Nutshell

Multiplex Networks of the Guarantee Market: Evidence from China

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