I've seen that by standardizing variables with subtracting their means, the VIF drops significantly below threshold of 5. But originally they were >10. What's the mathematical proves that standardization eliminate structural multi-collinearity problem?
I'm relatively new here, so I can't put this as a comment. The answer to the question that mlofton linked is great, and I believe it is line with this paper:
Iacobucci, D., Schneider, M. J., Popovich, D. L., & Bakamitsos, G. A. (2016). Mean centering helps alleviate “micro” but not “macro” multicollinearity. Behavior research methods, 48(4), 1308-1317.
Basically, standardizing variable doesn't help the model as a whole, but it can reduce VIF.