  In this paper, 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 (Structured-POET), incorporates observation structural information
for both global and national factor models. We establish the asymptotic
properties of the Structured-POET estimator, and also demonstrate the drawbacks
of conventional covariance matrix estimation procedures when using
lower-frequency data. Finally, we apply the Structured-POET estimator to an
out-of-sample portfolio allocation study using international stock market data.
