Bayesian parametric and semiparametric factor models for large realized covariance matrices
提出一种基于似然估计的因子结构，用于建模大型实现协方差矩阵，包含参数和非参数版本，通过并行计算可在几分钟内完成估计，适用于10到60个资产的数据。
Summary This paper introduces a new factor structure suitable for modeling large realized covariance matrices with full likelihood‐based estimation. Parametric and nonparametric versions are introduced. Because of the computational advantages of our approach, we can model the factor nonparametrically as a Dirichlet process mixture or as an infinite hidden Markov mixture, which leads to an infinite mixture of inverse‐Wishart distributions. Applications to 10 assets and 60 assets show that the models perform well. By exploiting parallel computing the models can be estimated in a matter of a few minutes.