  L\'evy processes are widely used in financial mathematics, telecommunication,
economics, queueing theory and natural sciences for modelling. We propose an
essentially asymptotically efficient estimation method for the system
parameters of general autoregressive conditional heteroscedasticity (GARCH)
processes. As an alternative to the maximum likelihood (ML) method we develop
and analyze a novel identification method by adapting the so-called empirical
characteristic function method (ECF) originally devised for estimating
parameters of c.f.-s from i.i.d. samples. Precise characterization of the
errors of these estimators will be given, and their asymptotic covariance
matrices will be obtained.
