LARGE GLOBAL VOLATILITY MATRIX ANALYSIS BASED ON OBSERVATION STRUCTURAL INFORMATION
提出一种名为S-POET的新方法，利用观测结构信息估计全球金融市场的波动率矩阵，解决了因使用低频数据导致的信息损失问题，并通过国际股市数据验证了其在投资组合配置中的有效性。
In this article, we develop a novel large volatility matrix estimation procedure for analyzing global financial markets. Practitioners often use lower-frequency data, such as weekly or monthly returns, to address the issue of different trading hours in the international financial market. However, this approach can lead to inefficiency due to information loss. To mitigate this problem, our proposed method, called Structured Principal Orthogonal complEment Thresholding (S-POET), incorporates observation structural information for both global and national factor models. We establish the asymptotic properties of the S-POET estimator, and also demonstrate the drawbacks of conventional covariance matrix estimation procedures when using lower-frequency data. Finally, we apply the S-POET estimator to an out-of-sample portfolio allocation study using international stock market data.