Garch fitting with mean regressors

"SB" == "Stefano Balietti" <futur.dorko at gmail.com>
on Tue, 15 Apr 2008 21:27:39 +0200

SB> Hi,
   SB> I'm looking for R packages able to perform GARCH-like fitting
   SB> (estimates),
   SB> such as fGarch, tseries and FinTS, but that allow me to
   SB> specify extra
   SB> regressors for the mean equation. It doesn't seem to me
   SB> that the
   SB> above-mentioned packages permit that, but maybe I'm
   SB> wrong. However,  does
   SB> anyone have any suggestion?
   SB> 
   SB> Cheers,
   SB> 
   SB> Stefano Balietti
   SB> 
  
As far as fGarch is concerned, you can only specify the arma regressors
for the mean equation. However, the functions in fGarch are quite
modular and it should not be too much of work to add new regressor to
the mean equation. 

regards,
Yohan

The 2nd International R/Rmetrics User and Developer Workshop ...
[http://www.rmetrics.org]

You can do the regression on the returns and
then fit the garch model on the residuals.  That
will most probably be very close to the result
if you did it "right".


Patrick Burns
patrick at burns-stat.com
+44 (0)20 8525 0696
http://www.burns-stat.com
(home of S Poetry and "A Guide for the Unwilling S User")

Hi,
I'm looking for R packages able to perform GARCH-like fitting (estimates),
such as fGarch, tseries and FinTS, but that allow me to specify extra
regressors for the mean equation. It doesn't seem to me that the
above-mentioned packages permit that, but maybe I'm wrong. However,  does
anyone have any suggestion?

Cheers,

Stefano Balietti

	[[alternative HTML version deleted]]

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On Wed, 16 Apr 2008 10:11:27 +0100

You can do the regression on the returns and
then fit the garch model on the residuals.  That
will most probably be very close to the result
if you did it "right".

I wonder if you could really do that. After all you would do an
estimation ignoring heteroscedasticity in the returns which biases the
parameter estimates. If you include the exogenous in the mean equation
of a garch model then you take conditional heteroscedasticity into
account. This is easy to do in most commercial software (e.g. EViews,
RATS etc.)

Zeno

On Wed, 16 Apr 2008 10:11:27 +0100
 Patrick Burns <patrick at burns-stat.com> wrote:
  

You can do the regression on the returns and
then fit the garch model on the residuals.  That
will most probably be very close to the result
if you did it "right".


    

I wonder if you could really do that. After all you would do an
estimation ignoring heteroscedasticity in the returns which biases the
parameter estimates. If you include the exogenous in the mean equation
of a garch model then you take conditional heteroscedasticity into
account. This is easy to do in most commercial software (e.g. EViews,
RATS etc.)

Zeno
  

Of course we can really do that.  The question is
whether or not it is a good idea to do it.

Yes, we are ignoring heteroscedasticity in the regression.
This makes it inefficient, but bias should be minimal.  There
is also the option to iterate the two stages which, under
suitable conditions, will converge to the maximum likelihood
solution.

If we are worried about violating assumptions, then the two
stage estimation is likely to be one of our lesser sins in the
exercise.

Pat

Yes, you can do this. Heteroskedasticity does not generally bias the
coefficients from the regression - just invalidates the usual standard
errors. For basic garch models you can estimate them in a two-step fashion.
Engle showed this in his orignal ARCH paper in 1982  

-----Original Message-----
From: r-sig-finance-bounces at stat.math.ethz.ch
[mailto:r-sig-finance-bounces at stat.math.ethz.ch] On Behalf Of Zeno Adams
Sent: Wednesday, April 16, 2008 6:37 AM
To: Patrick Burns; Stefano Balietti
Cc: r-sig-finance at stat.math.ethz.ch
Subject: Re: [R-SIG-Finance] Garch fitting with mean regressors

On Wed, 16 Apr 2008 10:11:27 +0100

You can do the regression on the returns and then fit the garch model 
on the residuals.  That will most probably be very close to the result 
if you did it "right".

I wonder if you could really do that. After all you would do an estimation
ignoring heteroscedasticity in the returns which biases the parameter
estimates. If you include the exogenous in the mean equation of a garch
model then you take conditional heteroscedasticity into account. This is
easy to do in most commercial software (e.g. EViews, RATS etc.)

Zeno

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