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!
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