A Comparison of Seasonal Adjustment Methods When Forecasting Intraday Volatility

In this study we compare volatility forecasts over a thirty-minute horizon for the spot exchange rates of the Deutsche Mark and the Japanese Yen against the US dollar. Explicitly modeling the intraday seasonal pattern improves the out-of-sample forecasting performance. We find that a seasonal estimated from the log of squared returns improves upon the use of simple squared returns, and that the Flexible Fourier Form (FFF) is an efficient way of determining the seasonal. The two-step approach that first estimates the seasonal using the FFF and then the parameters of the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model for the deseasonalized returns performs only marginally worse than the computationally expensive periodic GARCH model that includes the FFF