  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.
