activity
19992010
most citedCorrelation, hierarchies, and networks in financial markets

389 citations · 767 across the 15 of their papers we have counts for

collaborators

13 papers

physics.data-an2010★ 23 cited

Segmentation algorithm for non-stationary compound Poisson processes

Bence Toth, Fabrizio Lillo, J. Doyne Farmer

We introduce an algorithm for the segmentation of a class of regime switching processes. The segmentation algorithm is a non parametric statistical method able to identify the regi…

q-fin.ST2008★ 389 cited

Correlation, hierarchies, and networks in financial markets

M. Tumminello, F. Lillo, R. N. Mantegna

We discuss some methods to quantitatively investigate the properties of correlation matrices. Correlation matrices play an important role in portfolio optimization and in several o…

q-bio.QM2008★ 1 cited

Statistical properties of thermodynamically predicted RNA secondary structures in viral genomes

Marco Spanò, Fabrizio Lillo, Salvatore Miccichè +1

By performing a comprehensive study on 1832 segments of 1212 complete genomes of viruses, we show that in viral genomes the hairpin structures of thermodynamically predicted RNA se…

physics.comp-ph2008★ 2 cited

Generation of hierarchically correlated multivariate symbolic sequences

Mi. Tumminello, F. Lillo, R. N. Mantegna

We introduce an algorithm to generate multivariate series of symbols from a finite alphabet with a given hierarchical structure of similarities. The target hierarchical structure o…

physics.soc-ph2006★ 15 cited

Economic sector identification in a set of stocks traded at the New York Stock Exchange: a comparative analysis

C. Coronnello, M. Tumminello, F. Lillo +2

We review some methods recently used in the literature to detect the existence of a certain degree of common behavior of stock returns belonging to the same economic sector. Specif…

cond-mat.dis-nn2005★ 54 cited

Hierarchically nested factor model from multivariate data

M. Tumminello, F. Lillo, R. N. Mantegna

We show how to achieve a statistical description of the hierarchical structure of a multivariate data set. Specifically we show that the similarity matrix resulting from a hierarch…