Demystification of GARCH modeling with fGarch

[Yohan Chalabi]

Dear all,

I am working on a tutorial which would focus on the  common issues in GARCH/APARCH modeling. The idea is to give hints how to choose the optimization parameters,  the starting values, the distribution and how to properly scale the data.

This tutorial is meant to be very practical and I would like to have some input from the r-sig-finance community. If you have examples where garchFit badly failed for you, it would be great if you could send me your dataset with the R code you used. If you have any other comments or questions about fGarch, feel free to write me.

Thanks!

Regards,
Yohan

--
PhD student
Swiss Federal Institute of Technology
Zurich

www.ethz.ch
www.rmetrics.org

NOTE:
Rmetrics Workshop: http://www.rmetrics.org/meielisalp.htm
June 29th - July 3rd Meielisalp, Lake Thune, Switzerland

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[statquant]

Just to let you know, Eric Zivot has a fairly recent and interesting paper
on arch/garch modeling at his website. I haven't read it carefully yet ( I
more just glanced through it )  but maybe you'd want to look at that paper
before you make your tutorial. Thanks for all your work.


                                             Mark

-----Original Message-----
From: r-sig-finance-bounces@...
[mailto:r-sig-finance-bounces@...] On Behalf Of Yohan Chalabi
Sent: Thursday, June 05, 2008 4:20 AM
To: r-sig-finance@...
Subject: [R-SIG-Finance] Demystification of GARCH modeling with fGarch

Dear all,

I am working on a tutorial which would focus on the  common issues in
GARCH/APARCH modeling. The idea is to give hints how to choose the
optimization parameters,  the starting values, the distribution and how to
properly scale the data.

This tutorial is meant to be very practical and I would like to have some
input from the r-sig-finance community. If you have examples where garchFit
badly failed for you, it would be great if you could send me your dataset
with the R code you used. If you have any other comments or questions about
fGarch, feel free to write me.

Thanks!

Regards,
Yohan

--
PhD student
Swiss Federal Institute of Technology
Zurich

www.ethz.ch
www.rmetrics.org

NOTE:
Rmetrics Workshop: http://www.rmetrics.org/meielisalp.htm
June 29th - July 3rd Meielisalp, Lake Thune, Switzerland

_______________________________________________
R-SIG-Finance@... mailing list
https://stat.ethz.ch/mailman/listinfo/r-sig-finance
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-- If you want to post, subscribe first.

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[DavidM.UK]

The choice of distribution is relatively straight forward I'd say, in the Econometric literature it tends to be either a standardized Student's-t distribution [Bollerslev did a paper on it if I recall] or the standard normal distribution. As a practical measure, I don't think you could do much better than using qq.plot() from the "car" library for diagnostics, of course you might just look at the histrogram plot of the returns to see if their are heavy tails though (hist(x, br="FD") is my usual approach there).

For initial parameter estimates, I'm not sure it's a real issue, doesn't fGarch take care of that for you. I think MATLAB's GARCH Toolbox and RMetrics set \alpha_{i=1}^{p} = \frac{0.05}{p} and \beta_{j=1}^{q} = \frac{0.85}{q} . I tend to use those setting with my own GARCH models and they're generally "okay".

You might want to cover how to assess the fit of your estimated GARCH model, paying attention to the kurtosis and skewness statistics (routines to calculate both are in the e1071 package). As I'm sure you know, a strong negative skew probably means you'll have more sucess with something like GJR-GARCH. If the kurtosis is massively high (which you'll get if you looking at intraday data) there's not that much you can do about it (well it's what I work on) but I guess depending on your objective you might try and remove some of the extremes/outliers before estimating your model, I think fExtremes might have some useful stuff in that area.

Scaling the data is an interesting one, and a common cause of failure during the opitmization stage in my view. I tend to scale raw logged returns by 10, and intraday data by 100, but that's a rather random approach by me :) I'd definitely suggest you get a range of data, and not just use the EuStockMarkets series, for me I'd present GARCH modeling for intraday, daily and indice data, and they are quite different.

Unfortunately the data I'm using can't be shared, but I'm sure there are others out their that see fGarch fail with
>my.garch <- garchFit(data=x, forumla=~garch(1,1))
where x are logged returns and the garchFit seems to get stuck in a loop, with the only solution to quit R (on Linux this is).

It might be worth giving a few examples of the different types of optimization routines you can use, I don't tend to use RMetrics code - but the way my code works is pretty much the same, and I generally get faster convergence with nlminb() over optim(), and fGarch is generally quicker than my code anyhow. Does it have some sort of SQP routine?

Cheers

David

Yohan Chalabi wrote:

Dear all,

I am working on a tutorial which would focus on the  common issues in GARCH/APARCH modeling. The idea is to give hints how to choose the optimization parameters,  the starting values, the distribution and how to properly scale the data.

This tutorial is meant to be very practical and I would like to have some input from the r-sig-finance community. If you have examples where garchFit badly failed for you, it would be great if you could send me your dataset with the R code you used. If you have any other comments or questions about fGarch, feel free to write me.




Thanks!

Regards,
Yohan

--
PhD student
Swiss Federal Institute of Technology
Zurich

www.ethz.ch
www.rmetrics.org

NOTE:
Rmetrics Workshop: http://www.rmetrics.org/meielisalp.htm
June 29th - July 3rd Meielisalp, Lake Thune, Switzerland

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