The general-to-specific (GETS) methodology is widely employed in the modelling of
economic series, but less so in financial volatility modelling due to computational
complexity when many explanatory variables are involved. This study proposes a
simple way of avoiding this problem when the conditional mean can appropriately be
restricted to zero, and undertakes an out-of-sample forecast evaluation of the
methodology applied to the modelling of weekly exchange rate volatility. Our findings
suggest that GETS specifications perform comparatively well in both ex post and ex
ante forecasting as long as sufficient care is taken with respect to functional form and
with respect to how the conditioning information is used. Also, our forecast comparison
provides an example of a discrete time explanatory model being more accurate than
realised volatility ex post in 1 step forecasting
