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

[Spencer Graves]

Dear Yohan: 

      Thanks for your work on this. 

      If you haven't already, could you please add descriptions of the 
arguments delta, skew and shape to the help page:  How are they defined, 
e.g., relative to what distribution -- and which parameterization of 
that distribution?  For example, are these passed to documented R 
function(s)?  Also or alternatively, can you site a relevant Wikipedia 
article? 

      In particular, can Student's t be obtained as a special case?  I 
need this for my work with the FinTS package. 

      Thanks again,
      Spencer Graves

Yohan Chalabi wrote:
>>>>> "AZ" == Achim Zeileis <Achim.Zeileis at wu-wien.ac.at>
>>>>> on Wed, 13 Feb 2008 22:22:58 +0100 (CET)
>>>>>           
>
>
>    AZ> As far as I can see, Diethelm and Yohan have been quite busy
>    AZ> improving
>    AZ> the optimizers and also their documentation (which seems to
>    AZ> be more
>    AZ> detailed in the devel-version of the package, maybe Yohan
>    AZ> or Diethelm
>    AZ> can comment on this). I think (but might be wrong here) that all
>    AZ> optimizers used by garchFit() rely on numerical gradients
>    AZ> and numerical
>    AZ> Hessians.
>
> 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.
>
> regards,
> Yohan
>