A complexity gap computed as the normalized largest eigenvalue minus average pairwise correlation collapses to zero during shocks and shows a false-recovery phase before true restoration, predicting higher future volatility when low.
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Consensus clustering of price correlation networks with ARIMA forecasts produces cryptocurrency portfolios that deliver stable positive returns and tighter tail-risk control up to 14-day holding periods.
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Structural Dynamics of G5 Stock Markets During Exogenous Shocks: A Random Matrix Theory-Based Complexity Gap Approach
A complexity gap computed as the normalized largest eigenvalue minus average pairwise correlation collapses to zero during shocks and shows a false-recovery phase before true restoration, predicting higher future volatility when low.
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Optimising cryptocurrency portfolios through stable clustering of price correlation networks
Consensus clustering of price correlation networks with ARIMA forecasts produces cryptocurrency portfolios that deliver stable positive returns and tighter tail-risk control up to 14-day holding periods.