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Using non-linear models to forecast volatility for three equity index samples, this study
examines weekly returns of three indices; Dow Jones Industrial index, FTSE 100 index,
and Nikkei 225 index. The sample covers a twenty year sample period. The study
employs an in sample and out of sample volatility forecast using standard symmetric
loss functions in order to identify an appropriate model that best forecast volatility.
Using the mean error (ME), root mean square error (RMSE), mean absolute error
(MAE), and mean absolute percentage error (MAPE), the study finds the EGARCH
model to outperform the ARCH, and GARCH model in forecasting volatility.
