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Dive into the research topics where Nicol 'o Musmeci is active.

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Featured researches published by Nicol 'o Musmeci.


PLOS ONE | 2015

Relation between Financial Market Structure and the Real Economy: Comparison between Clustering Methods

Nicol 'o Musmeci; Tomaso Aste; T. Di Matteo

We quantify the amount of information filtered by different hierarchical clustering methods on correlations between stock returns comparing the clustering structure with the underlying industrial activity classification. We apply, for the first time to financial data, a novel hierarchical clustering approach, the Directed Bubble Hierarchical Tree and we compare it with other methods including the Linkage and k-medoids. By taking the industrial sector classification of stocks as a benchmark partition, we evaluate how the different methods retrieve this classification. The results show that the Directed Bubble Hierarchical Tree can outperform other methods, being able to retrieve more information with fewer clusters. Moreover, we show that the economic information is hidden at different levels of the hierarchical structures depending on the clustering method. The dynamical analysis on a rolling window also reveals that the different methods show different degrees of sensitivity to events affecting financial markets, like crises. These results can be of interest for all the applications of clustering methods to portfolio optimization and risk hedging.


Complexity | 2017

The multiplex dependency structure of financial markets

Nicol 'o Musmeci; Vincenzo Nicosia; Tomaso Aste; Tiziana Di Matteo; Vito Latora

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.


Scientific Reports | 2016

Interplay between past market correlation structure changes and future volatility outbursts

Nicol 'o Musmeci; Tomaso Aste; T. Di Matteo

We report significant relations between past changes in the market correlation structure and future changes in the market volatility. This relation is made evident by using a measure of “correlation structure persistence” on correlation-based information filtering networks that quantifies the rate of change of the market dependence structure. We also measured changes in the correlation structure by means of a “metacorrelation” that measures a lagged correlation between correlation matrices computed over different time windows. Both methods show a deep interplay between past changes in correlation structure and future changes in volatility and we demonstrate they can anticipate market risk variations and this can be used to better forecast portfolio risk. Notably, these methods overcome the curse of dimensionality that limits the applicability of traditional econometric tools to portfolios made of a large number of assets. We report on forecasting performances and statistical significance of both methods for two different equity datasets. We also identify an optimal region of parameters in terms of True Positive and False Positive trade-off, through a ROC curve analysis. We find that this forecasting method is robust and it outperforms logistic regression predictors based on past volatility only. Moreover the temporal analysis indicates that methods based on correlation structural persistence are able to adapt to abrupt changes in the market, such as financial crises, more rapidly than methods based on past volatility.


social informatics | 2014

Reconstructing topological properties of complex networks using the fitness model

Giulio Cimini; Tiziano Squartini; Nicol 'o Musmeci; Michelangelo Puliga; Andrea Gabrielli; Diego Garlaschelli; Stefano Battiston; Guido Caldarelli

A major problem in the study of complex socioeconomic systems is represented by privacy issues—that can put severe limitations on the amount of accessible information, forcing to build models on the basis of incomplete knowledge. In this paper we investigate a novel method to reconstruct global topological properties of a complex network starting from limited information. This method uses the knowledge of an intrinsic property of the nodes (indicated as fitness), and the number of connections of only a limited subset of nodes, in order to generate an ensemble of exponential random graphs that are representative of the real systems and that can be used to estimate its topological properties. Here we focus in particular on reconstructing the most basic properties that are commonly used to describe a network: density of links, assortativity, clustering. We test the method on both benchmark synthetic networks and real economic and financial systems, finding a remarkable robustness with respect to the number of nodes used for calibration. The method thus represents a valuable tool for gaining insights on privacy-protected systems.


Journal of Statistical Physics | 2013

Bootstrapping Topological Properties and Systemic Risk of Complex Networks Using the Fitness Model

Nicol 'o Musmeci; Stefano Battiston; Guido Caldarelli; Michelangelo Puliga; Andrea Gabrielli


arXiv: Portfolio Management | 2015

Risk diversification: a study of persistence with a filtered correlation-network approach

Nicol 'o Musmeci; Tomaso Aste; Tiziana Di Matteo


Nonlinear Dynamics, Psychology, and Life Sciences | 2016

Synchronization Analysis of Language and Physiology in Human Dyads.

Franco Orsucci; Nicol 'o Musmeci; Benjamin Aas; Günter Schiepek; Mario Antonio Reda; Luca Canestri; Giulio de Felice


European Physical Journal-special Topics | 2016

Two different flavours of complexity in financial data

R.J. Buonocore; Nicol 'o Musmeci; Tomaso Aste; T. Di Matteo


arXiv: Portfolio Management | 2016

What does past correlation structure tell us about the future? An answer from network filtering

Nicol 'o Musmeci; Tomaso Aste; Tiziana Di Matteo


PLOS ONE | 2015

Composition of clustering in terms of ICB supersectors.

Nicol 'o Musmeci; Tomaso Aste; T. Di Matteo

Collaboration


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Tomaso Aste

University College London

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Andrea Gabrielli

Sapienza University of Rome

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Guido Caldarelli

IMT Institute for Advanced Studies Lucca

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Michelangelo Puliga

IMT Institute for Advanced Studies Lucca

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Franco Orsucci

University College London

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Vincenzo Nicosia

Queen Mary University of London

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