[R-SIG-Finance] RUGARCH rolling forecast using external regressors

[alexios ghalanos]

Hi Stoyan,

1. Yes, that was definitely a bug, thanks for reporting. I have uploaded 
a fix to R-Forge (rev.423)...should be available to download in the next 
check/build.

2. Use the  "persistence" method on the fitted object:
 >persistence(fit)
persistence
    0.991474
The TARCH model persistence is NOT \alpha+\beta+\eta. See the vignette 
for the details (from the fGARCH model equation on persistence). Note 
that in all models of the rugarch package, the persistence constraint is 
turned ON by default (via the fit.control option).

3. Again, please read the vignette which clearly explains variance 
targeting (e.g. see page 6 equation 10 and the reference to the Engle 
and Mezrich (1995) paper on page 5).


HTH.

Regards,

Alexios


On 22/06/2012 15:02, stoyan.stoyanov wrote:
> Hi there,
>
> First of all, thanks for an amazing package! This is cutting edge and it
> really does wonders.
> *3 questions:*
> 1. I am using the rugarch package to fit a TARCH model to high frequency
> single stock returns data (2000 obs in the example below), and forecast
> conditional variance. Everything works fine until the moment when I try to
> do a rolling forecast using external regressors. Examples of regressors I
> have tried are implied volatility and an earnings event dummy variable. I
> noticed that someone else on the forum had a similar issue and was wondering
> whether there might be a glitch in the package or am I doing something
> wrong…
> Below are the relevant parts of the code, sample dataset and error message.
> Hope someone can help!
>
> 2. I am not convinced whether the fact that my TARCH alpha, beta and eta
> coefficients sum up to more than 1 means that I could get a non-stationary
> process. I know that with GARCH the coefficients sum should be restricted to
> <= 1 but am confused when it comes to the eta coefficient. (fit output at
> the bottom)
>
> 3. What exactly does variance targeting do. (Feel free to ignore this
> question)
> Thank you in advance.
>
> *#Code *
> spec<-ugarchspec(variance.model = list(model = "fGARCH", garchOrder =
> c(1,1),
>                          submodel = "TGARCH", external.regressors =
> regressors, variance.targeting = TRUE),
>                   mean.model = list (armaOrder = c(1,1), include.mean = TRUE,
> 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 = 0, solver = "solnp", solver.control
> = list(),
>                 fit.control = list(stationarity = 1, fixed.se = 0, scale =
> 0))
>
> roll=ugarchroll(spec, data, n.ahead = 1, forecast.length = 200, refit.every
> = 25,
>             refit.window = "moving", parallel = FALSE,
>             parallel.control = list(pkg = c("multicore", "snowfall"), cores =
> 2), solver = "solnp",
>             fit.control = list(), solver.control = list(), calculate.VaR =
> TRUE,
>             VaR.alpha = c(0.01, 0.05))
>
> *#Error message*
> ...estimating refit windows...
>
> Done!...all converged.
> Error in fmexdata[[i]] : subscript out of bounds
>
> #Again, my suspicion is that the rolling forecast function messes up because
> of the external regressors. It works perfectly fine without them...
>
> *---------------------------------*
> *          GARCH Model Fit        *
> *---------------------------------*
>
> Conditional Variance Dynamics 	
> -----------------------------------
> GARCH Model	: fGARCH(1,1)
> fGARCH Sub-Model	: TGARCH
> Mean Model	: ARFIMA(1,0,1)
> Distribution	: std
>
> Optimal Parameters
> ------------------------------------
>          Estimate  Std. Error   t value Pr(>|t|)
> mu      0.000015    0.000002   7.59458   0.0000
> ar1     0.183552    0.008229  22.30584   0.0000
> ma1    -0.223085    0.008187 -27.24730   0.0000
> alpha1  0.079457    0.003750  21.18601   0.0000
> beta1   0.931504    0.003484 267.37350   0.0000
> eta11   0.030469    0.005535   5.50526   0.0000
> vxreg1  0.000000    0.000007   0.00226   0.9982
> shape   6.453012    0.280138  23.03508   0.0000
> omega   0.000002          NA        NA       NA
>
> Robust Standard Errors:
>          Estimate  Std. Error    t value Pr(>|t|)
> mu      0.000015    0.000003   4.317851 0.000016
> ar1     0.183552    0.023880   7.686537 0.000000
> ma1    -0.223085    0.023552  -9.472028 0.000000
> alpha1  0.079457    0.005819  13.654150 0.000000
> beta1   0.931504    0.005007 186.045420 0.000000
> eta11   0.030469    0.003464   8.796946 0.000000
> vxreg1  0.000000    0.000010   0.001628 0.998701
> shape   6.453012    0.360340  17.908115 0.000000
> omega   0.000002          NA         NA       NA
>
> LogLikelihood : 127062.8
>
> http://r.789695.n4.nabble.com/file/n4634210/regressors.csv regressors.csv
> http://r.789695.n4.nabble.com/file/n4634210/data.csv data.csv
>
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>
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