[R-SIG-Finance] RUGARCH variance targeting issue

[alexios ghalanos]

Hi Stoyan and thanks for catching another one.

Bug quashed (rev.427)

You can also submit bug reports directly to the rugarch bug forum list:
https://r-forge.r-project.org/forum/forum.php?forum_id=1117

Regards,

Alexios

PS if you want the unconditional variance this can be correctly 
recovered by using the "uncvariance" method on the fitted object.

On 27/06/2012 15:50, stoyan.stoyanov wrote:
> Hi again,
>
> I am running a TGARCH(1,1) model on a series of adjusted daily stock returns
> and get significantly different forecasts when using variance targeting and
> when not. However, I feel that the forecast should not be that different.
> Could it be that the model is "targeting" the wrong variable (omega instead
> of unconditional variance)? Again it could well be a problem with my data or
> model, but I do not see anything obviously wrong with either. That's why I'm
> attaching the output under both scenarios, as well as my data. The relevant
> parts of the code are below. Hopefully you can help. I just don't think that
> my long term prediction of sigma should be going to 0...
>
> *Code:*
>
> spec=ugarchspec(variance.model = list(model = "fGARCH", garchOrder = c(1,1),
>                          submodel = "TGARCH", external.regressors =
> regressors, variance.targeting = TRUE/FALSE),
>                   mean.model = list (armaOrder = c(3,3), include.mean = mean,
> archm = FALSE,
>                          archpow = 1, arfima = FALSE, external.regressors =
> NULL, archex = FALSE),
>                   distribution.model = "std", start.pars = list(), fixed.pars
> = list())
>
> fit=ugarchfit(spec, data, out.sample = 100, solver = "solnp", solver.control
> = list(),
>                 fit.control = list(stationarity = 1, fixed.se = 0, scale =
> 0))
>
> forecast=ugarchforecast(fit, data = NULL, n.ahead = 100, n.roll = 50,
> out.sample = 0,
>                 external.forecasts = list(mregfor = NULL, vregfor = NULL))
>
> boot.pred=ugarchboot(fit, data = NULL, method = "partial", n.ahead = 10,
>                  n.bootfit = 100, n.bootpred = 500, out.sample = 0, rseed =
> NA, solver = "solnp",
>                  solver.control = list(), fit.control = list(),
>                  external.forecasts = list(mregfor = NULL, vregfor = NULL),
> parallel = FALSE,
>                  parallel.control = list(pkg = c("multicore", "snowfall"),
> cores = 2))
>
> P.S. I know that I probably don't need an ARMA(3,3).
>
> *Data + Ouput files:*
> http://r.789695.n4.nabble.com/file/n4634631/var.targeting.conditional.forecast.jpeg
> var.targeting.conditional.forecast.jpeg
> http://r.789695.n4.nabble.com/file/n4634631/var.targeting.bootstrap.output.jpg.jpeg
> var.targeting.bootstrap.output.jpg.jpeg
> http://r.789695.n4.nabble.com/file/n4634631/no.var_targeting.conditional_forecast.jpeg
> no.var_targeting.conditional_forecast.jpeg
> http://r.789695.n4.nabble.com/file/n4634631/no.var.targeting.bootstrap.output.jpg
> no.var.targeting.bootstrap.output.jpg
> http://r.789695.n4.nabble.com/file/n4634631/var.targeting.fit.txt
> var.targeting.fit.txt
> http://r.789695.n4.nabble.com/file/n4634631/no.var.targeting.fit.txt
> no.var.targeting.fit.txt
>
> Thanks,
> Stoyan
>
> -----
> Stoyan Stoyanov
> The University of Chicago Booth School of Business
> MBA Class of 2013
> (312) 532-0120 | stoyanov at chicagobooth.edu
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