Testing and Valuing Dynamic Correlations for Asset Allocation
用经济损失函数评估方差和相关性模型，通过构建最小化预测方差的投资组合，发现正确设定的协方差矩阵能最小化已实现波动率，并检验动态相关性在资产配置中的价值。
We evaluate alternative models of variances and correlations with an economic loss function. We construct portfolios to minimize predicted variance subject to a required return. It is shown that the realized volatility is smallest for the correctly specified covariance matrix for any vector of expected returns. A test of relative performance of two covariance matrices is based on work of Diebold and Mariano. The method is applied to stocks and bonds and then to highly correlated assets. On average, dynamically correct correlations are worth around 60 basis points in annualized terms, but on some days they may be worth hundreds.