The article discusses how to predict the volatility of financial prices using a method called Realized Range Volatility. By considering noise in high-frequency data and accounting for patterns in financial data, the researchers created a model that accurately forecasts volatility. They found that including asymmetric effects in the model significantly improved the accuracy of predicting future price movements.