We evaluate the forecasting performance of time series models for realized volatility, which accommodate long memory, level shifts, leverage effects, day-of-the-week and holiday effects, as well as macroeconomic news announcements. Applying the models to daily realized volatility for the S&P 500 futures index, we find that explicitly accounting for these stylized facts of volatility improves out-of-sample forecast accuracy for horizons up to 20 days ahead. Capturing the long memory feature of realized volatility by means of a flexible high-order AR-approximation instead of a parsimonious but stringent fractionally integrated specification also leads to improvements in forecast accuracy, especially for longer horizon forecasts.

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Keywords Day-of-the-week effect, Leverage effect, Long memory, Macroeconomic news announcements, Model confidence set, Realized volatility, Volatility forecasting
Persistent URL dx.doi.org/10.1016/j.ijforecast.2009.01.010, hdl.handle.net/1765/18152
Citation
Martens, M.P.E., van Dijk, D.J.C., & de Pooter, M.D.. (2009). Forecasting S&P 500 volatility: Long memory, level shifts, leverage effects, day-of-the-week seasonality, and macroeconomic announcements. International Journal of Forecasting, 25(2), 282–303. doi:10.1016/j.ijforecast.2009.01.010