The article discusses how using high-frequency data to estimate daily volatility can improve option pricing accuracy. By modeling Realized Volatility as an Inverse Gaussian variable with a changing mean, the researchers developed a more effective option pricing model. Empirical analysis on options for three major American stocks showed that this new model outperformed traditional models based on daily returns alone. This suggests that using high-frequency data can lead to better predictions in option pricing.