[R-SIG-Finance] Alternative solvers in rugarch (was: GARCH parameter estimation with rugarch: estimates seem inaccurate)
e@ @end|ng |rom enr|co@chum@nn@net
Fri Feb 1 14:28:04 CET 2019
>>>>> "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> 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.
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:
>> 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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