[R-SIG-Finance] garch vs garchFit - minimum sample size

[Achim Zeileis]

Yohan,

thanks for info!

> We have implemented a new optimization scheme  "mnfb" in the
> devel-version of fGarch
> (https://svn.r-project.org/Rmetrics/trunk/fGarch). It is actually the
> same fortran library as used in the R function nlminb(). But we have
> implemented the whole optimization in fortran.
>
> As you have noticed it, we are also working on the documentation and we
> hope the new manual page is more readable.
>
>    AZ> Adrian's code comes with its own optimzer (Quasi-Newton) which
>    AZ> is not
>    AZ> available in garchFit() (I think) and provides both analytical
>    AZ> and
>    AZ> numerical gradients (Gaussian conditional distribution only).
>
> In garchFit you can choose between 5 different optimizations
> schemes : "nlminb" , "mnfb" (in devel-version), "sqp", "lbfgsb",
> "nlminb+nm", "lbfgsb+nm". Please read the man page for more details.
>
> Although the analytical gradient and hessian of ARMA-APARCH for
> Gaussian conditional distribution can be  calculated without too
> much of effort, the analytical solutions for other
> distribution are not trivial. Since garchFit can handle different
> conditional distributions ("norm", "snorm", "ged", "sged", "std",
> "sstd"), we decieded to use only numerical approximations.

Ah, yes, I should have written that in my post. In garch() it is possible
to get the analytical gradients because it uses the Gaussian conditional
distribution. And I wasn't suggesting to provide it for all distributions
supported by garchFit(). But for the Gaussian case, it would be a nice
addition for garchFit() to support analytical gradients/Hessian.

Best wishes,
Z