st: t-GARCH/ GARCH-models with conditional non-normality

["Roman Goldbach" <Roman_Goldbach@uni-konstanz.de>]

Dear All,

I am conducting a time series study on credit risks in EMU-bonds; the data  
show clear autocorrelation, unconditional non-normality (excessive  
skewnesss and excess kurtosis) and ARCH-processes. Hence I decided for a  
GARCH-type approach.

However, the generic GARCH(1/1) delivers non-normal residuals, i.e.  
conditional non-normality exists. This is said to be typical for financial  
time series. Acoording to Campell, Lo, MacKinlay (1997: 488-9) three  
possibilities exist:
- use the generic GARCH(1/1), but with robust standard errors - several  
studies report, that this leads to unbiased estimators and reliable  
p-values.
2) model another distribution, which accounts for the fat tails - most  
often a form of t-distribution; Bollerslev (1987) did that
3) use a semi-parametric approach, as in Engle and Gonzalez-Rivera (1991)

Is it possible to calculate a t-GarCH (or other GARCH based on the  
assumption of a t-type distribution) within STATA ? - I was not able to  
answer this question positively myself; However, the basic approach - as  
discussed in Bollerslev (1987) seems 'pretty simple' to me - "The model  
can be derived as a simple subordinate stochastic process by including an  
additive unobservable error term in the conditional variance equation."
I'm just a user, hence I am not aware, how I could do this myself in STATA.

Questions
1) Can STATA calculate a t-GARCH, or alike model?
2) If yes, how?
3) Is STATA able to run a semiparametric GARCH?
4) If yes, how?
5) Which of the three approaches (according to some experience) would you  
follow?

Thanks very much in advance.

Roman
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