The study shows that volatility clustering is not only found in high-frequency financial data but also in monthly stock returns. By using a modeling approach based on GARCH models, the researchers discovered significant serial dependence in the second moments of monthly data. They found that estimating low-frequency models using temporal aggregation reduces parameter uncertainty significantly.