[R-SIG-Finance] rugarch: External Regressor

[alexios galanos]

Here's a reproducible example on how you can pass starting parameters 
and bounds:

library(rugarch)
library(xts)
data("dmbp")
dmbp <- as.xts(dmbp, as.Date(1:nrow(dmbp)))
spec <- ugarchspec(mean.model = list(armaOrder = c(0,0), include.mean = 
TRUE),
                    variance.model = list(model = "sGARCH", garchOrder = 
c(1,1), external.regressors = dmbp[,2]))

setstart(spec) <- list("vxreg1" = 0.5)
setbounds(spec) <- list("vxreg1" = c(0, 1))
spec using model$start.pars
mod <- ugarchfit(spec, dmbp[,1])

I suspect this may also be related to a parameter scaling issue as well. 
I'm working on a new implementation which should hopefully fix
this and many other issues (autodiff gradients/hessian, option for 
multiplicative regressors, parameter scaling. standard error calculation
options etc), but won't have something out for at least a few months (it 
will eventually be located in this repo: github.com/tsmodels/).

Regards,

Alexios


On 1/11/23 12:31 PM, Simon van Norden wrote:
>
> Having trouble with bad initial parameter estimates in rugarch when 
> using an external regressor.
>
>   * Convergence code is 0 and convergence is rapid, but….
>   * Coefficient estimate on the external regressor is always 0.00000
>     and condH = NaN
>   * Results are robust to variable scaling and optimization algorithm
>     (tried them all.)
>
> My hunch is that I’m on a saddle point (or similar ‘flat spot’.)
>
> I’d like to try alternative initial parameter estimates for the 
> external regressor; can anyone point me to an example showing how to 
> pass this to the optimizer?
>
> Would to happy to hear of other suggestions, or provide details, a 
> reprex, etc.
>
> FWIW, this may be related to the question someone else posted back on 
> 20-06-2022.
>
> Thnx
>
> *Simon van Norden*
>
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>
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