Garch fitting with mean regressors

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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