Forecasting realized volatility using a long-memory stochastic volatility model: estimation, prediction and seasonal adjustment

We study the modeling of large data sets of high-frequency returns using a long-memory stochastic volatility (LMSV) model. Issues pertaining to estimation and forecasting of large data sets using the LMSV model are studied in detail. Furthermore, a new method of de-seasonalizing the volatility in high-frequency data is proposed, that allows for slowly varying seasonality. Using both simulated as well as real data, we compare the forecasting performance of the LMSV model for forecasting realized volatility (RV) to that of a linear long-memory model fit to the log RV. The performance of the new seasonal adjustment is also compared to a recently proposed procedure using real data.