[R-SIG-Finance] "ugarchfit" function of "rugarch" package needs at least 100 data points.

Eric Zivot ezivot at u.washington.edu
Tue Oct 9 22:22:41 CEST 2012

While GARCH parameter estimates can be quite variable (as Pat has nicely
shown), the resulting volatility forecasts tend to be much more stable (at
least in the short term). If the goal is short-term vol forecasting then one
doesn't really care too much about the GARCH point estimates. The important
long-term parameters are the unconditional vol and the persistence of the
GARCH process as these dictate where and how fast the vol forecasts evolve.
For the short term, most GARCH forecasts look a lot like simple EWMAs. So if
you only have 100 obvs and want to get a simple short-term forecast, use an
EWMA. That's the riskMetrics approach.

-----Original Message-----
From: r-sig-finance-bounces at r-project.org
[mailto:r-sig-finance-bounces at r-project.org] On Behalf Of Patrick Burns
Sent: Tuesday, October 09, 2012 1:05 PM
To: r-sig-finance at r-project.org
Subject: Re: [R-SIG-Finance] "ugarchfit" function of "rugarch" package needs
at least 100 data points.

The blog post:


shows how variable garch results are using
2000 daily observations.

Even if there is a way to get your model fit, I doubt that it would mean
very much.

If you do find a way, I would suggest that you create multiple datasets that
are simulations of the model the same size as your data.  Then estimate the
model on the simulations and see how variable those model estimates are.
They will be very variable, I predict.


On 09/10/2012 19:58, Tanvir Khan wrote:
> Right now I'm trying to fit a GJR Garch model on the inflation rate of 
> my country, Bangladesh, but the problem is the function "ugarchfit" of 
> the "rugarch" package requires at least 100 data points to run. I have 
> yearly
> (12 months average) data and my country is not even 50 years old! So, 
> is there any way to use this function to fit a data with less than 100 
> observations? Any type of suggestion will be extremely helpful.

Patrick Burns
patrick at burns-stat.com
twitter: @portfolioprobe

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