---
abstract: 'We discuss some methods to quantitatively investigate the properties of correlation matrices. Correlation matrices play an important role in portfolio optimization and in several other quantitative descriptions of asset price dynamics in financial markets. Specifically, we discuss how to define and obtain hierarchical trees, correlation based trees and networks from a correlation matrix. The hierarchical clustering and other procedures performed on the correlation matrix to detect statistically reliable aspects of the correlation matrix are seen as filtering procedures of the correlation matrix. We also discuss a method to associate a hierarchically nested factor model to a hierarchical tree obtained from a correlation matrix. The information retained in filtering procedures and its stability with respect to statistical fluctuations is quantified by using the Kullback-Leibler distance.'
address:
- 'Dipartimento di Fisica e Tecnologie Relative, Università di Palermo, Viale delle Scienze, I-90128 Palermo, Italy'
- 'Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, U.S.A.'
author:
- Michele Tumminello
- Fabrizio Lillo
- 'Rosario N. Mantegna'
title: 'Correlation, hierarchies, and networks in financial markets'
---

multivariate analysis, hierarchical clustering, correlation based networks, bootstrap validation, factor models, Kullback-Leibler distance.

JEL classification: C32, G10

Introduction {#intro}
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Many complex systems observed 