Unveiling the connectivity structure of financial networks via high-frequency analysis

Donatello Materassi, Giacomo Innocenti

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


The paper deals with the problem of reconstructing the internal link structure of a network of agents subject to mutual dependencies. We show that standard multivariate approaches based on a correlation analysis are not well suited to detect mutual influences and dependencies, especially in the presence of delayed or propagative relations and when the sampling rate is sufficiently high to capture them. In particular, we develop and apply a metric based on the coherence function to take into account these dynamical phenomena. The effectiveness of the proposed approach is illustrated through numerical examples and through the analysis of a real complex networked system, i.e. a set of 100 high volume stocks of the New York Stock Exchange, observed during March 2008 and sampled at high frequency.

Original languageEnglish (US)
Pages (from-to)3866-3878
Number of pages13
JournalPhysica A: Statistical Mechanics and its Applications
Issue number18
StatePublished - Sep 15 2009


  • Econophysics
  • Multivariate analysis
  • Stock market analysis
  • Topology identification


Dive into the research topics of 'Unveiling the connectivity structure of financial networks via high-frequency analysis'. Together they form a unique fingerprint.

Cite this