[R-SIG-Finance] another garch question

[babel at centrum.sk]

Hello scientists
How can I quantify the garch model? The equations are for example from book Analysis of financial time series by Tsay
rt = 0.0067 + at,     at = σt * et 
 σ2t = 0.000119 + 0.8059σ 2t−1 + 0.1941a2t−1

fit = garchFit(~garch(1, 1), data =ret)

What slot holds the et variable in R? (fit at residuals ??? I doubt) In every book I read there was only coefficients estimated, but no graphical output of modelled serie produced. So how can I said that for example model garch(1,1) is better than arch(2)? ArchTest,LM test and BDS test of the standardized residuals are the only options? or AIC, SIC?

I tried 2 models
fit1 = garchFit(~arma(1, 0)+garch(1,1), data =ret)
fit2 = garchFit(~arma(1, 0)+garch(2,1), data =ret)
and the fit1, fit2 at fitted were slightly different, so the volatility (garch term) must influence somehow to mean estimation. 
Why so many books ended with coefficients estimation?And what tells me the predicted standard deviation about the future behaviour of return series? Are there good only for making confidence intarvals +-2*sd or to print simga.t and said so, this is my volatility?
So how correctly use garch in modelling time series and comparing it with different methods if garch is for volatility and not for mean?

Sorry for slow thinking :)))