Beyond GMV: the relevance of covariance matrix estimation for risk-based portfolio construction
实证分析不同协方差矩阵估计方法在构建股票投资组合时的实际效果，发现当组合加入约束和交易成本后，简单样本估计与复杂方法表现相近，但考虑时间序列动态仍有助于降低波动。
We empirically analyze the relevance of variance-covariance (VCV) estimators in equity portfolio construction. While traditional analyses of unconstrained global minimum-variance (GMV) portfolios support using shrinkage and modeling covariance dynamics, the resulting portfolios are often impractical due to high leverage, concentration, and costs. By examining constrained GMV and risk parity portfolios, we find a significantly reduced opportunity for alternative VCV estimators to outperform the sample estimator. Specifically, we show that a long-only portfolio with asset-level constraints and a transaction cost penalty produces similar results to shrinkage-based methods, even when using the sample VCV estimator. However, accounting for time-series dynamics in asset returns remains statistically relevant for volatility reduction. Our findings emphasize how the interaction between VCV estimators and portfolio construction choices shapes both the statistical and practical outcomes in portfolio management.