  We develop two new estimators for a general class of stationary GARCH models
with possibly heavy tailed asymmetrically distributed errors, covering
processes with symmetric and asymmetric feedback like GARCH, Asymmetric GARCH,
VGARCH and Quadratic GARCH. The first estimator arises from negligibly trimming
QML criterion equations according to error extremes. The second imbeds
negligibly transformed errors into QML score equations for a Method of Moments
estimator. In this case, we exploit a sub-class of redescending transforms that
includes tail-trimming and functions popular in the robust estimation
literature, and we re-center the transformed errors to minimize small sample
bias. The negligible transforms allow both identification of the true parameter
and asymptotic normality. We present a consistent estimator of the covariance
matrix that permits classic inference without knowledge of the rate of
convergence. A simulation study shows both of our estimators trump existing
ones for sharpness and approximate normality including QML, Log-LAD, and two
types of non-Gaussian QML (Laplace and Power-Law). Finally, we apply the
tail-trimmed QML estimator to financial data.
