The study looked at using high frequency data from financial markets to predict future volatility in the Standard & Poor's 100 stock index. Researchers compared different models and found that models using high frequency data were more accurate in forecasting volatility than models that did not use this information. Specifically, models based on realized volatility and extended Stochastic Volatility were the most effective in predicting volatility over short to medium time horizons.