We investigate the planar maximally filtered graphs of the portfolio of the 300 most capitalized stocks traded at the New York Stock Exchange during the time period 2001–2003. Topological properties such as the average length of shortest paths, the betweenness and the degree are computed on different planar maximally filtered graphs generated by sampling the returns at different time horizons ranging from 5 min up to one trading day. This analysis confirms that the selected stocks compose a hierarchical system progressively structuring as the sampling time horizon increases. Finally, a cluster formation, associated to economic sectors, is quantitatively investigated
We review the recent approach of correlation based networks of financial equities. We investigate po...
Abstract. Networks of companies can be constructed by using return correlations. A crucial issue in ...
The final publication is available at Springer via DOI 10.1007/s10614-012-9327-x with the title: A G...
We investigate the planar maximally filtered graphs of the portfolio of the 300 most capitalized sto...
Two kinds of filtered networks: minimum spanning trees (MSTs) and planar maximally filtered graphs (...
We apply a method to filter relevant information from the correlation coefficient matrix by extracti...
Networks of companies can be constructed by using return correlations. A crucial issue in this appro...
The hierarchical structure of correlation matrices in complex systems is studied by extracting a sig...
We review a correlation based clustering procedure applied to a portfolio of assets synchronously tr...
This thesis discusses how properties of complex network theory can be used to study financial time s...
BACKGROUND: In this paper we investigate the definition and formation of financial networks. Specifi...
Financial markets can be represented as complex networks of agents connected by different intensitie...
We investigate the emergence of a structure in the correlation matrix of assets' returns as the time...
What are the dominant stocks which drive the correlations present among stocks traded in a stock mar...
In this paper, networks of S&P 500 stocks are constructed based on the correlation matrices of daily...
We review the recent approach of correlation based networks of financial equities. We investigate po...
Abstract. Networks of companies can be constructed by using return correlations. A crucial issue in ...
The final publication is available at Springer via DOI 10.1007/s10614-012-9327-x with the title: A G...
We investigate the planar maximally filtered graphs of the portfolio of the 300 most capitalized sto...
Two kinds of filtered networks: minimum spanning trees (MSTs) and planar maximally filtered graphs (...
We apply a method to filter relevant information from the correlation coefficient matrix by extracti...
Networks of companies can be constructed by using return correlations. A crucial issue in this appro...
The hierarchical structure of correlation matrices in complex systems is studied by extracting a sig...
We review a correlation based clustering procedure applied to a portfolio of assets synchronously tr...
This thesis discusses how properties of complex network theory can be used to study financial time s...
BACKGROUND: In this paper we investigate the definition and formation of financial networks. Specifi...
Financial markets can be represented as complex networks of agents connected by different intensitie...
We investigate the emergence of a structure in the correlation matrix of assets' returns as the time...
What are the dominant stocks which drive the correlations present among stocks traded in a stock mar...
In this paper, networks of S&P 500 stocks are constructed based on the correlation matrices of daily...
We review the recent approach of correlation based networks of financial equities. We investigate po...
Abstract. Networks of companies can be constructed by using return correlations. A crucial issue in ...
The final publication is available at Springer via DOI 10.1007/s10614-012-9327-x with the title: A G...