Estimating integrated volatility using absolute high-frequency returns

When high-frequency data is available, in the context of a stochastic volatility model, realised absolute variation can estimate integrated spot volatility. A central limit theory enables us to do filtering and smoothing using model-based and model-free approaches in order to improve the precision of these estimators. Although the absolute values are empirically attractive as they are less sensitive to possible large movements in high-frequency data, realised absolute variation does not estimate integrated variance. Some problems arise when using a finite number of intra-day observations, as explained here.