How to check which fitted Copula based on parametric marginal distribution is the best?

Dear Sir/Madam

I am trying to fit the Copula to the data, regarding to the parametric marginal distribution. As I found the way that you introduced in MathWorks is Nonparametric based on finding Kernel;

u = ksdensity(x,x,'function','cdf'); v = ksdensity(y,y,'function','cdf');

In my case, I have two variables that allow me to think they are distributed under a Normal Distributions, so as you say, I use the normcdf to obtain the array U(0,1), and afterwards use the copulafit on this.

My questions, are two:

a) With this copulafit what I obtained are the parameters of the copula that have this marginals (eg, in the case of a t, the parameters would be the linear correlation and the degrees of freedom). Therefore, the parameters that characterize my copula. Is this right?

b) On the other hand, how can I choose which Copula fits better with my data, I have to measure the dependence among both variables, so I guess the one which shows me this dependence. How can I do it? is there any way of doing it visually?

Thank you very much.

[Antonio]

It would be possible to apply the empirical copula in order to check which copula fits better? In case this is so, how can I implement it?

Thank you