高维协方差矩阵的分层DCC-HEAVY模型
Hierarchical DCC-HEAVY Model for High-Dimensional Covariance Matrices
AI总结：
本文提出HD DCC-HEAVY分层因子模型，利用高频已实现测度估计高维协方差矩阵，方法简单且独立于资产维度，实证显示其样本内外表现优于现有模型。
摘要：
我们引入了一类用于高维协方差矩阵的HD DCC-HEAVY分层型因子模型，该模型利用了由更高频数据构建的已实现测度。该建模方法具有直接的估计和预测方案，与所考虑资产的横截面维度无关，并考虑了协方差中复杂的非对称动态。实证分析表明，相对于现有的分层因子模型和标准基准，HD DCC-HEAVY模型具有更好的样本内拟合，并带来统计上和经济上显著的样本外收益。结果在不同频率和市场条件下均具有稳健性。
英文摘要：
We introduce a HD DCC-HEAVY class of hierarchical-type factor models for high-dimensional covariance matrices, employing the realized measures built from higher-frequency data. The modelling approach features straightforward estimation and forecasting schemes, independent of the cross-sectional dimension of the assets under consideration, and accounts for sophisticated asymmetric dynamics in the covariances. Empirical analyses suggest that the HD DCC-HEAVY models have a better in-sample fit and deliver statistically and economically significant out-of-sample gains relative to the existing hierarchical factor model and standard benchmarks. The results are robust under different frequencies and market conditions.
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