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st: t-GARCH/ GARCH-models with conditional non-normality
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
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
- use the generic GARCH(1/1), but with robust standard errors - several
studies report, that this leads to unbiased estimators and reliable
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.
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
Thanks very much in advance.
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