Cluster formation and evolution in networks of financial market indices

TL;DR

This paper analyzes how clusters of global stock indices form and evolve over time, especially during financial crises, by examining correlations and eigenvector structures across different periods and thresholds.

Contribution

It introduces a method to study cluster dynamics in financial networks using correlation matrices and eigenvector analysis, highlighting market-specific structures during crises.

Findings

Clusters change significantly during crises

Eigenvector analysis reveals market-specific structures

Correlation-based clusters vary with thresholds and time

Abstract

Using data from world stock exchange indices prior to and during periods of global financial crises, clusters and networks of indices are built for different thresholds and diverse periods of time, so that it is then possible to analyze how clusters are formed according to correlations among indices and how they evolve in time, particularly during times of financial crises. Further analysis is made on the eigenvectors corresponding to the second highest eigenvalues of the correlation matrices, revealing a structure peculiar to markets that operate in different time zones.