Mean-variance portfolio optimization requires both invertible and well-conditioned covariance matrices. This paper compares the performance of covariance conditioning techniques applied to the realized covariance matrices of the portfolio of constituents of the Dow Jones Industrial Average. We use the volatility of portfolio returns derived from volatility-timing investment strategies employing dierent conditioning techniques as the criterion of assessment. As portfolio dimensions increase there is increasing need for matrix conditioning to maintain the precision improvement oered by intraday data. We nd that the relative performance of the single factor model provides a computationally tractable alternative to fully estimated realized covariance matrices in a global minimum variance dynamic portfolio setting.