The article introduces new ways to measure and predict extreme events in financial markets and economic growth. It presents a simple method called TailCoR to capture correlations during financial crises, showing that both linear and non-linear correlations play a role. The study also identifies non-normal shocks in economic models, helping to understand the impact of unexpected events on the economy. The findings suggest that co-movement among major US banks increased during the financial crisis due to both linear and non-linear correlations, with non-linear factors driving risks associated with the European sovereign debt crisis.