Q- How does Reverse optimization and Black Litterman solve the GIGO problem ? How does using the global market portfolio weights improve the input of expected returns of asset classes? Can someone explain
So reverse MVO incorporates into the process covariances and exp returns based on the global portfolio, which is assumed to have an optimal asset allocation.
Black Litterman is the same as reverse MVO with the exception of allowing to incorporate investors expectations about the global markets. This way, the client folio can be aligned the way the client wants.
In the MVO , is the portfolio manager creating his OWN expectations of the global portfolio ? or Are there consensus estimates and expectations of its returns ?
MVO doesn’t work on global portfolios. MVO is an own estimation of variances, returns, etc.
Reverse MVO assumes a global portfolio, which is considered having an optimal allocation of funds. It starts from deriving variances, exp returns, etc from global folio and then moves into normal MVO, which will now have allocation based on an optimally allocated folio.