Article ID: | iaor20118657 |
Volume: | 27 |
Issue: | 4 |
Start Page Number: | 1089 |
End Page Number: | 1107 |
Publication Date: | Oct 2011 |
Journal: | International Journal of Forecasting |
Authors: | Chortareas Georgios, Jiang Ying, Nankervis John C |
Keywords: | forecasting: applications, statistics: inference |
We assess the performances of alternative procedures for forecasting the daily volatility of the euro’s bilateral exchange rates using 15 min data. We use realized volatility and traditional time series volatility models. Our results indicate that using high‐frequency data and considering their long memory dimension enhances the performance of volatility forecasts significantly. We find that the intraday FIGARCH model and the ARFIMA model outperform other traditional models for all exchange rate series.