The EGARCH is a popular model for discrete time volatility since it allows
for asymmetric effects and naturally ensures positivity even when including
exogenous variables. Estimation and inference is usually done via maximum
likelihood. Although some progress has been made recently, a complete
distribution theory of MLE for EGARCH models is still missing. Furthermore,
the estimation procedure itself may be highly sensitive to starting values,
the choice of numerical optimation algorithm, etc. We present an alternative
estimator that is available in a simple closed form and which could be used,
for example, as starting values for MLE. The estimator of the dynamic
parameter is independent of the innovation distribution. For the other
parameters we assume that the innovation distribution belongs to the class
of Generalized Error Distributions (GED), profiling out its parameter in the
estimation procedure. We discuss the properties of the proposed estimator
and illustrate its performance in a simulation study.