I wanna evaluate a simple GARCH(1,1) model for the conditional variance. Firstly, I understand that the conditional variance is unobserved and that is really the crux of the issue.
Out-of-sample, I have created a loop that re-estimates, for each new observation, and forecasts one period ahead. This works just fine, yet I am unsure of what I should evaluate this against. I first though the squared residuals, but then again? Of what model should these come from? The model with same specifications, but for the whole sample?
Edit: If any one else finds this question, I just wanted to say briefly what I found. When evaluating forecasts of different models, one can use squared returns (if working with stock returns). This might not give correct results (it actually almost surely will, because realized vol is not the same as squared returns), yet the ranking will as if the exercise had been done with the (unobservable) conditional variance.