The researchers studied how to improve a measure of market volatility by correcting for noise in the data. They found that their new method reduced errors by 50%-90% compared to the standard measure. However, they also discovered that the assumption of noise being independent and identically distributed (iid) is not true in real-world data. This could impact high-frequency volatility measures but not low-frequency ones.