At first glance I think that a better approach for this problem would be using an analysis technique called STATIS DUAL. That would reduce all your indexes, for all countries, for all years in a single matrix. But that may be more complicated
I have some questions regarding your PCA analysis. You are calculating the Principal Components from the Covariance Matrix. This is not common since this approach will give more importance to those indexes that fluctuate the most, that is the unstable ones. I've seen some economic studies with that objective, but I'd like to know is that's your case. Most PCA applications will be based in the correlation matrix instead. Now, If I understood well, you are trying to perform a regression model using the indexes returns as response variable and the principal components of return of countries as regressors. And you do that for every year? I probably would need to see your dataset in order to provide some further assistance, since I cannot see exactly what you are doing.
The rotated components are used to obtain a cleaner interpretation, keep in mind that PCA creates indexes and the eigenvectors let you know how each variable contributed to those indexes. You can use the scores as predictors in a model, that is a common practice.
Anyway, I would need to see your dataset first, at least an extract from it.