RT Journal Article SR Electronic T1 A Test of Covariance-Matrix Forecasting Methods JF The Journal of Portfolio Management FD Institutional Investor Journals SP 97 OP 108 DO 10.3905/jpm.2015.41.3.097 VO 41 IS 3 A1 Valeriy Zakamulin YR 2015 UL https://pm-research.com/content/41/3/97.abstract AB Providing a more accurate covariance matrix forecast can substantially improve the performance of optimized portfolios. Using out-of-sample tests, in this article the author evaluates alternative covariance matrix-forecasting methods by looking at: (1) their forecast accuracy, (2) their ability to track the volatility of a minimum-variance portfolio, and (3) their ability to keep the volatility of a minimum-variance portfolio at a target level. The author finds large differences between the methods. The results suggest that shrinking the sample covariance matrix improves neither the forecast accuracy nor the performance of minimum-variance portfolios. In contrast, switching from the sample covariance matrix forecast to a multivariate generalized autoregressive conditional heteroskedasticity (GARCH) forecast reduces the forecasting error and portfolio tracking error by at least half. The findings also reveal that the exponentially weighted covariance matrix forecast performs only slightly worse than the multivariate GARCH forecast.TOPICS: Portfolio management/multi-asset allocation, statistical methods