[R-SIG-Finance] GARCH parameter estimation with rugarch: estimates seem inaccurate

alexios ghalanos @|ex|o@ @end|ng |rom 4d@c@pe@com
Mon Jan 28 19:02:23 CET 2019

Hi Curtis,

There is a function in rugarch called ugarchdistribution for performing these types of experiments:

spec1 <- ugarchspec(mean.model = list(armaOrder = c(0,0), include.mean = FALSE),
                    fixed.pars = list("omega" = 0.2, "alpha1" = 0.2, "beta1" = 0.2))
d=ugarchdistribution(spec1, n.sim=2000, m.sim=100, recursive = TRUE, recursive.length = 6000, solver.control=list(trace=1))

Try this and perhaps also read this blog post:

Could we benefit from a better nonlinear solver? Perhaps. 
Could we benefit from code contributions to make it better? Definitely.
Feel free to contribute.



On Mon, 28 Jan 2019 16:23:09 +0000, Curtis Miller <cgmil using msn.com> wrote:

> Hello all,
> Over a year ago I wrote a blog post about the problems I was having 
> estimating the parameters of GARCH models via fGarch. I got a lot of 
> feedback and I've now followed up with another article taking that 
> feedback into account: 
> https://ntguardian.wordpress.com/2019/01/28/problems-estimating-garch-parameters-r-part-2-rugarch/
> First, I switched from fGarch to rugarch, which is supposedly still 
> maintained. I also looked at other parameter combinations in simulation 
> experiments that others requested.
> It seems that rugarch isn't necessarily better when it comes to 
> parameter accuracy and one needs a lot of data (in the order of 
> thousands) to get good estimates of the parameter values. That said, CIs 
> computed are highly unreliable even at large sample sizes and there is 
> certainly no "silver bullet" optimization algorithm.
> I'd like feedback if I'm not doing things right. I heard once that 
> others could not replicate my results; that is, they have reliable 
> estimates for GARCH parameters. But I never found out who those people 
> were and they did not give me their code to see what I was doing wrong.
> If the community is aware of better approaches, I would like to hear 
> them as well.
> Thank you all,
> Curtis Miller
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