Forecasting Large Realized Covariance Matrices: The Benefits of Factor Models and Shrinkage
提出一种预测大型已实现协方差矩阵的模型，通过分解协方差矩阵为常见公司层面因子和残差部分，并采用向量异质自回归模型结合LASSO收缩估计，提高了预测精度和最小方差组合估计效果。
Abstract We propose a model to forecast large realized covariance matrices of returns, applying it to the constituents of the S&P 500 daily. To address the curse of dimensionality, we decompose the return covariance matrix using standard firm-level factors (e.g., size, value, and profitability) and use sectoral restrictions in the residual covariance matrix. This restricted model is then estimated using vector heterogeneous autoregressive models with the least absolute shrinkage and selection operator. Our methodology improves forecasting precision relative to standard benchmarks and leads to better estimates of minimum variance portfolios.