Large Global Volatility Matrix Analysis Based on Observation Structural Information

Sung Hoon Choi and Donggyu Kim ()
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Donggyu Kim: Department of Economics, University of California Riverside

No 202424, Working Papers from University of California at Riverside, Department of Economics

Abstract: 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 (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.

Date: 2024-12
New Economics Papers: this item is included in nep-ets and nep-rmg
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