The fGARCH predict() method will do that with the n.ahead argument; no

need to supply newdata. Try running

> example('predict-methods', package="fGARCH")

to see an example (with plots!).

The standard ARMA and GARCH models don't need external data to make

predictions: their predictions are made by integrating over the shocks

(innovations), the distribution of which is specified by the fitted

model.

I think ugarchsim from the rugarch package sometimes needs external

regressors if you fit an ARMA-X mean model [ARMA with external

regressors], but that's because they have a new (non-shock) term that

you don't want to integrate over.

(More precisely, you probably do want to integrate over the predictive

distribution of your external regressors, but you don't want your

GARCH model doing that. Instead, you want something with a proper

model to give a predictive distribution over your external regressors

and then do use a conditioning argument to get your predictive

distribution over both the regressors and the shocks).

Cheers,

Michael

On Wed, Nov 2, 2016 at 3:21 AM, Be Water <

[hidden email]> wrote:

> Well, I just want to predict mean and variance for the next period based on the fitted model parameters.

> Could be I don't understand how GARCH models work.

> Thanks

>

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