Temporal Evolution of Financial Market Correlations

TL;DR

This paper analyzes how financial market correlations evolve over time, revealing structural changes and increased interconnectedness following the 2007-2008 crisis using random matrix theory and principal component analysis.

Contribution

It applies random matrix theory and PCA to study the temporal evolution of market correlations, highlighting structural changes during financial crises.

Findings

Correlation matrices contain non-random structure.

A few principal components explain most market variability.

Post-2008 crisis, market relationships significantly strengthened.

Abstract

We investigate financial market correlations using random matrix theory and principal component analysis. We use random matrix theory to demonstrate that correlation matrices of asset price changes contain structure that is incompatible with uncorrelated random price changes. We then identify the principal components of these correlation matrices and demonstrate that a small number of components accounts for a large proportion of the variability of the markets that we consider. We then characterize the time-evolving relationships between the different assets by investigating the correlations between the asset price time series and principal components. Using this approach, we uncover notable changes that occurred in financial markets and identify the assets that were significantly affected by these changes. We show in particular that there was an increase in the strength of the relationships between several different markets following the 2007--2008 credit and liquidity crisis.