The article explores why financial data often shows patterns of volatility over time. By looking at how past data influences future volatility, the researchers found that these patterns can be explained by nonlinear relationships within the data itself. This means that instead of adding extra models to explain volatility, focusing on the underlying data patterns may be more effective. The study suggests that even when complex modeling is not possible, considering past data in volatility analysis can still be valuable.