Quantile regression methods are suggested for a class of ARCH models.
Because conditional quantiles are readily interpretable in semiparamet
ric ARCH models and are inherently easier to estimate robustly than po
pulation moments, they offer some advantages over more familiar method
s based on Gaussian likelihoods. Related inference methods, including
the construction of prediction intervals, are also briefly discussed.