[R] calibration of Garch models to historical data

[Ivette]

I have done the usual estimation of GARCH models, applied to my historical
dataset (commodities futures) with a maximum likelihood function and
selected the best model on the basis of information criteria such as Akaike
and Bayes.

Can somebody explain me please the calibration scheme for a GARCH model? 

I was not able to find a paper, dealing with exactly this algorithm for my
case. I only understood that I have to compare the performance of the best
GARCH model (from the estimation step), fitted to my historical dataset and
a GARCH simulation (let's abbreviate this Squared Error difference to "E2").
However, it is not clear to me:
- with what parameters' values to start this simulation,
- how many times it is normal to perform it, and 
- what to compare via E2 (maximum likelihood values, or parameter values)
- how to construct&assess E2 for the GARCH case.

Thank you in advance for your suggestions.

Ivette

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