ARMAX, t-GARCH estimation(RUGARCH package)
It means that the standard errors could not be calculated as a result of not being able to invert the hessian during the post-estimation phase. As explained in the manual and vignette, general failures in the final optimization/post-estimation can generally be approached by changing the solver and/or tweaking the solver. Other suggestions: - DO use model 'sGARCH' rather than the 'GARCH' model of 'fGARCH' for estimation. - Because you include regressors in the variance equation, try to provide a starting parameter for those and also try eGARCH where positivity is guaranteed (possibly the source of the problem). Regards, Alexios
On Feb 11, 2012, at 10:44, ecsniffer?? <ecsniffer at hotmail.com> wrote:
I use ARMA(4,0), t-GARCH(1,1) model to estimate the volatility series(y_t). In the model, I add rate ( r_t ) and lagged rate( r_{t -1} ) to the conditional mean function and conditional variance function as the exogenous variables.
Here is my code:
data<-read.csv("...")
volatility<-data$volatility;
volatility.lag<-embed(volatility,2);
volatility<-as.matrix(volatility.lag[,1]);
rate<-data$diff_rate;
rate.lag<-embed(rate,2);
spec=ugarchspec(variance.model=list(model="fGARCH",garchOrder=c(1,1),submodel="GARCH",
external.regressors=as.matrix(rate.lag), variance.targeting = FALSE),
mean.model = list(armaOrder = c(4,0), include.mean = TRUE, garchInMean = FALSE,
inMeanType = 1, arfima = FALSE, external.regressors =as.matrix(rate.lag)), distribution.model = "std",
start.pars = list(), fixed.pars = list())
fit=ugarchfit(spec,data=volatility)
fit
I got some errors and can't figure out the reason. Thanks!
Iter: 1 fn: 2986.4841 Pars: -0.211418971918 0.097905377888 -0.084512900004 -0.053448303895 -0.071041833756 -22.798457638632 16.379426108878 1.626051893520 0.206719227655 0.747514215831 0.000000009905 0.000000009905 4.008451660273
Iter: 2 fn: 2986.4841 Pars: -0.211418971919 0.097906631452 -0.084513745760 -0.053447443169 -0.071041878730 -22.797651046488 16.377961083304 1.626059793075 0.206723362776 0.747509647714 0.000000009905 0.000000009905 4.008479595008
solnp--> Completed in 2 iterations
Warning:
In .makefitmodel(garchmodel = "fGARCH", f = .fgarchLLH, data = data, :
rugarch-->warning: failed to invert hessian
Errors in t.default(grad) : Parameters are not the matrix.
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