[R-SIG-Finance] Alternative solvers in rugarch (was: GARCH parameter estimation with rugarch: estimates seem inaccurate)

[Enrico Schumann]

>>>>> "alexios" == alexios ghalanos <alexios using 4dscape.com> writes:

    alexios> Hi Curtis,
    alexios> There is a function in rugarch called ugarchdistribution for performing these types of experiments:

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

    alexios> Try this and perhaps also read this blog post:
    alexios> http://www.unstarched.net/2012/12/26/garch-parameter-uncertainty-and-data-size/

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

    alexios> Best,

    alexios> Alexios

I am not using 'rugarch' and only had a brief look at
the code. But is it possible to "plug in" alternative
solvers, i.e. without changing the package code?

If not, that could be a useful feature, as it would
allow to quickly test solvers. An "external" solver
would have to comply with some interface convention,
i.e. the solver would have to be provided as
a function that takes certain defined input arguments
and evaluates to defined outputs.



    alexios> 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
    >> 

-- 
Enrico Schumann
Lucerne, Switzerland
http://enricoschumann.net