So, for example, if you had three series return1, return2 and return3, and three independent variables x1, x2 and x3, then the command mgarch dcc (return1 return2 = x1 x2, het(x1 x2 x3)) (return3 = x3, het(x1 x2 x3)) Which allows you to add explanatory variables to the conditional variance equation of any one, to sets of, or to all the dependent variables at the same time. The optional equation options, eqoptions, has the following optional component het(varlist) include varlist in the specification of the conditional variance The optional argument allows you to add explanatory variables to the conditional mean to any one, to sets of, or to all the dependent variables at the same time. This is explained in the help file for mgarch dcc. The OP has asked about adding explanatory variables in the equations for the conditional mean and the conditional variance. The xline option asks for a vertical line at t=2015 (in the previous example, this was to indicate that at t=2015, forecasts were out-of-sample), and the legend(row(3)) specifies that you want the graph legend to placed in three separate rows - this is just graph formatting. The option t>1600 asks that the in-sample predictions be plotted starting at "time period" 1600 (this dataset has a time index that runs from 1 to 2015). If you are only interested in the in-sample fits of the conditional correlations, then it becomes really simple, and you can drop the tsappend command: webuse stocks Which produces the graph of the dynamic correlations between the three individual stocks. Tsline H_nissan_toyota H_honda_toyota H_honda_nissan if t>1600, /// Mgarch dcc (toyota nissan =, noconstant) (honda =, noconstant), /// You will find the following example of computing the dynamic correlations and their forecasts. You need to read the help file for -mgarch dcc postestimation.
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