The documentation and vignette FAQs list a couple of options (please

read them). In order of likely importance:

1. Set tol<delta (in solver.control).

2. Use scaling (in fit.control).

3. Set stationarity to FALSE (sometimes this creates problems with the

solver for some boundary cases).

4. Use an alternate solver(s) (there are many now included).

5. Change the default solver parameters.

6. Use starting values.

7. Check your data for extreme outliers or numbers of zeros.

In the next release I will provide a hybrid solver optimization strategy

to rotate among the available solvers when this happens.

-Alexios

On 18/09/2012 16:57, Serdar Neslihanoglu wrote:

>

> My code: spec<-ugarchspec(variance.model = list(model = "sGARCH", garchOrder = c(1, 1),

submodel = "Null", external.regressors = NULL, variance.targeting = FALSE),

mean.model = list(armaOrder=c(0,0),include.mean =FALSE, archm = FALSE,

archpow = 1,

arfima = FALSE, external.regressors = NULL, archex = FALSE),

distribution.model = "norm",

start.pars = list(), fixed.pars = list())

ugarchfit(spec, X2, out.sample = 0, solver = "solnp", solver.control =

list(trace = TRUE, tol=1e-4, delta=1e-8),

fit.control = list(stationarity = 1, fixed.se = 0, scale=0))

I got this error massage In .makefitmodel(garchmodel = "sGARCH", f =

.sgarchLLH, ... :

rugarch-->warning: failed to invert hessian I think rugarch is not

converge the result properly.

Please let me know how to deal with this problem. Regards, Serdar

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