rugarch and fGarch

Marco,

All models (more precisely their filters which are used in the 
estimation process) in the rugarch package are already coded in C for speed.
The "apARCH" is an omnibus model and as such carries a penalty for such 
flexibility, as does the use of the 'sstd' distribution.
There is also a small penalty for the 1-stage estimation of 
ARFIMA-GARCH, rather than the marginally faster 2 stage estimation which 
I do not make use of in the package (but you can control for that by 
passing an arima filtered residual series using an appropriate 
specification and reconstituting later with fixed parameters in the spec 
for doing forecasting).
For the rolling estimation, you can make use of the parallel option to 
evaluate in parallel the rolling estimations/forecasts. See the FAQ 
section of the vignette for some comments on the tradeoff between the 
number of cores to use versus the size of the problem for the snowfall 
package. I have personally found that running things on linux with the 
multicore package is quite faster, but that may be because I do not have 
any optimized setup for windows R.

-Alexios

Hello Alexios,

thank you very much!

With the fit.control=list(scale=1) is also much faster.

I also added the multi-core support, is there any other way to improve the
performance?

Thank you very much!
Marco

--
View this message in context: http://r.789695.n4.nabble.com/rugarch-and-fGarch-tp4633077p4633368.html
Sent from the Rmetrics mailing list archive at Nabble.com.

_______________________________________________
R-SIG-Finance at 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.