Large dynamic covariance matrices and portfolio selection with a heterogeneous autoregressive model
提出一种通过异质自回归波动率和相关性成分来建模大维动态协方差矩阵的新框架，可直接预测月度协方差矩阵，并采用非线性收缩方法处理参数估计风险，实证显示在千只股票的最小方差组合中，年化标准差降至8.92%，优于DCC类模型。
We propose a novel framework for modeling large dynamic covariance matrices via heterogeneous autoregressive volatility and correlation components. Our model provides direct forecasts of monthly covariance matrices and is flexible, parsimonious and simple to estimate using standard least squares methods. We address the problem of parameter estimation risks by employing nonlinear shrinkage methods, making our framework applicable in high dimensions. We perform a comprehensive empirical out-of-sample analysis and find significant statistical and economic improvements over common benchmark models. For minimum variance portfolios with over a thousand stocks, the annualized portfolio standard deviation improves to 8.92% compared to 9.75–10.43% for DCC-type models.