I am trying to compare the performance of two minimum variance optimization (mvpo) methods applied on stocks Hierarchical risk parity (HRP) vs the analytical global minimum variance formula.
I feel like using naively chosen empirical data simulated data will affect the results in an unpredictable way. One of those factors is the magnitude of the covariance between assets, another is shocks to the stocks.
My question is if there is an accepted way of varying parameters like this in a simulation study where the covariance and mean matrix are manipulated. I am new to this field, so any specific research or part in a book would be appreciated.