Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
The general-to-specific (GETS) methodology is widely employed in the modelling of economic series, but less so in financial volatility modelling, due to its 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 the 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 the functional form and the way in which the conditioning information is used. Also, our forecast comparison provides an example of a discrete time explanatory model being more accurate than the realised volatility ex post in 1-step-ahead forecasting.