What does past correlation structure tell us about the future? An answer from network filtering

TL;DR

This paper introduces a novel network filtering approach using correlation structure persistence to forecast market volatility changes, outperforming traditional methods and adapting quickly to market crises.

Contribution

The paper presents a new correlation-based network filtering method and a measure for market dependence change, improving volatility forecasting especially in large portfolios.

Findings

The method predicts volatility changes with statistical significance.

It outperforms models based solely on past volatility.

It adapts rapidly during financial crises.

Abstract

We discovered that past changes in the market correlation structure are significantly related with future changes in the market volatility. By using correlation-based information filtering networks we device a new tool for forecasting the market volatility changes. In particular, we introduce a new measure, the "correlation structure persistence", that quantifies the rate of change of the market dependence structure. This measure shows a deep interplay with changes in volatility and we demonstrate it can anticipate market risk variations. Notably, our method overcomes the curse of dimensionality that limits the applicability of traditional econometric tools to portfolios made of a large number of assets. We report on forecasting performances and statistical significance of this tool for two different equity datasets. We also identify an optimal region of parameters in terms of True Positive and False Positive trade-off, through a ROC curve analysis. We find that our forecasting method is robust and it outperforms predictors based on past volatility only. Moreover the temporal analysis indicates that our method is able to adapt to abrupt changes in the market, such as financial crises, more rapidly than methods based on past volatility.