Empirical analysis and forecasting of volatility dynamics in high-frequency returns with time-varying components

We study intraday return volatility dynamics using a time-varying components approach, and the method is applied to analyze IBM intraday returns. Empirical evidence indicates that with three additive components - a time-varying mean of absolute returns and two cosine components with time-varying amplitudes - together they capture very well the pronounced periodicity and persistence behaviors exhibited in the empirical autocorrelation pattern of IBM returns. We find that the long-run volatility persistence is driven predominantly by daily level shifts in mean absolute returns. After adjusting for these intradaily components, the filtered returns behave much like a Gaussian noise, suggesting that the three-components structure is adequately specified. Furthermore, a new volatility measure (TCV) can be constructed from these components. Results from extensive out-of-sample rolling forecast experiments suggest that TCV fares well in predicting future volatility against alternative methods, including GARCH model, realized volatility and realized absolute value.