A0190
Title: Large global volatility matrix analysis based on structural information
Authors: Sung Hoon Choi - University of Connecticut (United States) [presenting]
Donggyu Kim - KAIST (Korea, South)
Abstract: A novel large volatility matrix estimation procedure is developed 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, the proposed method, called structured principal orthogonal complement thresholding (Structured-POET), incorporates structural information for both global and national factor models. The asymptotic properties of the structured-POET estimator are established and also demonstrate the drawbacks of conventional covariance matrix estimation procedures when using lower-frequency data. Finally, the structured-POET estimator is applied to an out-of-sample portfolio allocation study using international stock market data.
