I'm currently studying Copulas. However, i did not understand something. The very basic phases of Copula fitting is as follows i assume;
- Model each samples distribution with a parametric(or non-parametric) pdf.
- By using(or assuming) the model CDF, convert each RV to the [0,1] range.
- Fit a parametric copula to the converted RVs.
However, on the 4th step, all the examples on the Internet evaluates a regeneration phase from the fitted Copula. They basically sample new values from copula function and by using Inverse-CDF, they obtain real values in the domain of each original marginals.
What i do not understand is what is the practical reason for re-sampling phase? I already have some observations and by fitting a copula i can say that either "these two distributions are dependent and this(copula) is the model of their dependency" or "they seem to be independent (could not find any suitable copula)". However, what is the reason for re-generating some non-observed values as they are actually observed ? Where should I use these "synthetic" values in real life ?