Dear all,

Using RMetrics I've estimated a GARCH(1,1) model for a financial time
series, consisting of 6 years of daily stock return data. Apart from the
fact that garchFit seems to have problems estimating some parameters using a
skewed student's T distribution -I've posted another question regarding
this- the following also applies to a fitted GARCH(1,1) model with a
"regular" conditionally normal distribution error term.

After having fitted the parameters to my GARCH(1,1) model (without any AR
terms), the fitted model results in a straight line. If I fit parameters to
the same model with adding an AR(1)-term, the fitted model is exactly the
same as a single AR(1) model but slightly elevated.

Let me say this is the first time I try to estimate a GARCH model; since all
the articles -quite a lot- I have read laud GARCH' capability in prediction,
I am sure I am doing something wrong (probably interpretation-wise) since my
results look really useless to me. The estimated parameters seem to be okay,
because when I simulate a GARCH process using these the result is
satisfactory - very roughly comparable to a simulated stochastic volatility
model with Ornstein-Uhlenbeck volatility process (which is what I've done up
to now).

Any insight on this would be greatly appreciated!
Thanks a lot in advance,


Jeroen van der Heide

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