trubador wrote: DCC add-in does not include any diagnostic tools specific to this type of models, like EViews' regular objects. You need to figure it out on your own. If you have any doubts on DCC-type models, the add-in is not the right place to start. You need to study the background first.

Thanks for your insights trubador, I am trying hard to understand the model more deeply.

I thought that if applied the usual diagnostics to the garch univariate residuals, I could rely on the dcc estimation output of the add-in, but i suppose the best is to test the dcc model itself then.

If i choose

**normal distribution** the step 1 garch univariate estimation

does not converge (after 1000 iterations) for one of the series and the arch term is not significant, which i believe means that the model is a GARCH (0,1) but the add-in gives the output of the dcc anyway (and values do seem reasonable: alpha=0,052 and Beta=0,92)

Example: output with

**t-student distribution** is this:

Coefficient Std. Error z-Statistic Prob.

theta(1) 0.061770 0.040861 1.511716 0.1306

theta(2) 0.347048 0.443426 0.782651 0.4338

t-Distribution (Degree of Freedom)

theta(3) 3.649305 0.148316 24.60491 0.0000

Log likelihood 7872.354 Schwarz criterion -10.82304

Avg. log likelihood 2.722114 Hannan-Quinn criter. -10.85277

Akaike info criterion -10.87048

* Stability condition: theta(1) + theta(2) < 1 is met.

The values of Alpha and Beta don't seem in line with I've seen (typically Beta is around or higher than 0.90). The p-value for both coefficient is >0,05 so they aren't significant does this mean that the dcc model is not valid under the assumption of t distribution of the errors?

Trubador I'm sorry to bother you but your help is invaluable, and believe me i'm trying really hard to find the answers.