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Hi

I have found something that appears to be a bug. I noticed it when looking at long-term convergences in an estimated diagonal BEKK model for variances, covariances and correlations which did not correspond to what I was expecting to find. I am not fully sure where the issue comes from but just a few observations that might help to identify the problem. For instance using the example file: intl_fin.wf1 and the series sp500 I noticed the following: Comparing the estimated constant term of a pure OLS of the form: dlog(sp500) c reveals a difference to the constant (mean) estimated when adding a garch(1,1) specification to the variance part. Furthermore, when comparing the sample variance of the residuals of the garch(1,1) model - this does not correspond to the model implied long-term convergence level defined as omega/(1-alpha-beta). This might also translate into the problems in the MV specification.

I am using Eviews 8 with the 2 July 2014 patch.

Thanks in advance for looking into this.

Best regards
Rasmus

I'm not sure I follow.

Are you saying that you expect the estimate of the constant term from the following two equations to be the same?

yes, I would not expect the mean estimate, here c, to be impacted by the choice of variance specification

Unfortunately, it is.

ok, maybe I am just getting rusty - never noticed it changed in the past (which is not to say it did not...) the estimated mean, however, is not where I see the major problem, but that the unconditional variance of the residual do not match the level prescribed by the garch parameters, or maybe I am getting that wrong as well...

Which residuals?

those produced by the garch model, so y-c in your specification: equation eq1.arch y c

Probably just means that your model specification is wrong. It is an identity relationship.

Use the TSDGP add-in to generate a truly GARCH based variable, then do the same comparison. You should see that the two values are pretty darn close.

if you don't see an issue that is fine, thanks for your time :)