The article discusses a model called A-PARCH that helps predict changes in volatility in financial markets. This model uses a special transformation to estimate volatility from data, rather than assuming it beforehand. The researchers found that this model can be applied at different time intervals and can help understand how asset prices and volatility are related. This information is important for creating flexible models, identifying correlations, and predicting long-term market behavior.