Problem: Correct usage of GARCH(1,1)
Aim of research: Forecasting volatility/variance.
Tools used: Python 3.3 with arch library
I am trying to obtain out-of-sample estimation of volatility using a fitted GARCH (or other model from the library), so I can compare it with other approaches - like recurrent neural networks.
However, I haven't found a way, how to use the fitted model in similar way to the R as mentioned here, where they fit the model and then obtain rolling forecast one period ahead. I have looked through many examples and tutorial, but they always use in-sample estimates, when they already have the residuals as in example with in-sample estimation. I would like to "feed" the model historical data and obtain n periods ahead prediction (e.g. day ahead).
I believe the answer is very simple, however, I have not found it. Also, admittedly, I am quite new into using these types of models and python for statistical learning (I mostly used machine learning in the past).