  GARCH models are useful tools in the investigation of phenomena, where
volatility changes are prominent features, like most financial data. The
parameter estimation via quasi maximum likelihood (QMLE) and its properties are
by now well understood. However, there is a gap between practical applications
and the theory, as in reality there are usually not enough observations for the
limit results to be valid approximations. We try to fill this gap by this
paper, where the properties of a recent bootstrap methodology in the context of
GARCH modeling are revealed. The results are promising as it turns out that
this remarkably simple method has essentially the same limit distribution, as
the original estimatorwith the advantage of easy confidence interval
construction, as it is demonstrated in the paper.
  The finite-sample properties of the suggested estimators are investigated
through a simulation study, which ensures that the results are practically
applicable for sample sizes as low as a thousand. On the other hand, the
results are not 100% accurate until sample size reaches 100 thousands - but it
is shown that this property is not a feature of our bootstrap procedure only,
as it is shared by the original QMLE, too.
