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 <bwa...@outlook.com> 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 > _______________________________________________ R-SIG-Finance@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-finance -- Subscriber-posting only. If you want to post, subscribe first. -- Also note that this is not the r-help list where general R questions should go.