[R-SIG-Finance] Rugarch: How to do Iterated n-ahead Multistep Out-of-Sample GARCH Forecasts?

[Don Brady]

What would be the most efficient way to make iterated n-ahead Multistep 
Out-of-Sample GARCH Forecasts using Rugarch?

Let me explain what I mean.

A number of papers on using GARCH describe such a method and call it the 
"iterated" method. (They also say that it works very).
I am trying to use it.

For example, from
"Multi-Period Forecasts of Volatility:Direct, Iterated, and Mixed-Data 
Approaches"
by Eric Ghysels† Antonio Rubia‡ et al:

"Long horizon volatility forecasts can be constructed in three 
fundamentally different ways.
....... The second approach is to estimate a daily autoregressive 
volatility forecasting model and then
iterate over the daily forecasts for the necessary number of periods to 
obtain weekly, monthly, or quarterly predictions
of the volatility. The forecasting literature refers to the first 
approach as “direct” and
the second as “iterated” (Marcellino, Stock, and Watson (2006))."
http://www.unc.edu/~eghysels/papers/Var_9.pdf

I am looking to use this "iterated" approach to make a long term forecast.

The authors do not appear to be referring to a simulation, but rather 
are making an out of sample iterative forecast that ends up cumulatively 
giving them a forecast for up to 30 days ahead.

I can see that this could be done in rugarch by using an R loop, 
stepping forward out of sample one day at a time. At each step of the 
loop, one would call ugarchfit, then call ugarchforecast with a one day 
horizon. Then for the next iteration of the loop in R, one would augment 
the data by the result of the just-performed forecast, and re-fit and 
re-forecast etc.

However, this might be slow so I was just wondering if there is a 
rugarch-built-in way of doing this without needing the outer loop in R.

ugarchforecast does offer n.ahead forecasts, but states that n-step 
ahead (n>1) forecasts are based on the unconditional expectation of the 
models, which does not seem to be the same thing as these authors are 
suggesting.

I just have the feeling that I am missing something.

THANK YOU for any comments and also for providing this incredibly 
comprehensive package!