[R-SIG-Finance] Problems when estimating GARCH parameters with fGarch

[Vivek Rao]

If the likelihoods for the "erratic" parameter estimates are higher than for the
estimates you consider more plausible, then the program is doing what it should.
Have you checked this? If the parameter estimates do not maximize the likelihood,
can you call the fGARCH or RUGARCH estimation functions with different starting
points? This is a common approach to global optimization.

Vivek Rao
Boston, MA
________________________________
From: Curtis Miller <cgmil at msn.com>
To: "r-sig-finance at r-project.org" <r-sig-finance at r-project.org> 
Sent: Saturday, November 4, 2017 1:57 PM
Subject: Re: [R-SIG-Finance] Problems when estimating GARCH parameters with    fGarch


I only recently found out about rugarch. I�m going to try and look into that soon. I tried a quick test on a simulated time series and it seemed to have some similar behavior to what I observed with fGarch but I have not done as complete an investigation.



Curtis



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Today's Topics:

   1. Re: Problems when estimating GARCH parameters with        fGarch
      (Robert Harlow)


----------------------------------------------------------------------

Message: 1
Date: Fri, 3 Nov 2017 14:45:43 -0600
From: Robert Harlow <rharlow86 at gmail.com>
To: Curtis Miller <cgmil at msn.com>
Cc: "r-sig-finance at r-project.org" <r-sig-finance at r-project.org>
Subject: Re: [R-SIG-Finance] Problems when estimating GARCH parameters
        with    fGarch
Message-ID: <BBD3BA73-6E57-41CC-ACDC-5095F30C715A at gmail.com>
Content-Type: text/plain;       charset=us-ascii

Have you tried rugarch or just "garch" in the tseries package?  Reproducible examples are always helpful as well.

> On Nov 2, 2017, at 1:56 PM, Curtis Miller <cgmil at msn.com> wrote:
>
> Hello all,
>
> I have encountered bad behavior in fGarch's garchFit() function used for
> estimating the parameters of a GARCH model. The estimates behave in
> highly erratic ways on simulated data. For example, when beta = 0.2
> according to the simulation, the function sometimes estimates beta to be
> 0.0000001 even for sample sizes as large as 1000, and there are other
> irregularities. I believe this behavior is tied to how the numerical
> optimizers are computing the parameters.
>
> In my research I planned on using garchFit() from fGarch in a
> changepoint detection context. I was hoping to use it to detect
> structural change in GARCH parameters. (See, for example, Ling 2007
> paper https://arxiv.org/abs/0708.2369 .) But with this behavior I don't
> know if such a test using garchFit() is possible; the estimates are too
> unreliable.
>
> Has anyone else observed this behavior? Is there a way to get around it?
> I'm hoping someone who knows more about this can offer guidance.
>
> I have written a blog post documenting the behavior I observed, with
> numerical experiments. Here is a link:
> https://ntguardian.wordpress.com/2017/11/02/problems-estimating-garch-parameters-r/
>
>
> Curtis
>
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