Large Global Volatility Matrix Analysis Based on Observation Structural Information

26 Pages Posted: 12 May 2023 Last revised: 20 Feb 2024

See all articles by Sung Hoon Choi

Sung Hoon Choi

University of Connecticut

Donggyu Kim

College of Business, Korea Advanced Institute of Science and Technology (KAIST)

Date Written: May 2, 2023

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 (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.

Suggested Citation

Choi, Sung Hoon and Kim, Donggyu, Large Global Volatility Matrix Analysis Based on Observation Structural Information (May 2, 2023). KAIST College of Business Working Paper Series, Available at SSRN: https://ssrn.com/abstract=4436084 or http://dx.doi.org/10.2139/ssrn.4436084

Sung Hoon Choi (Contact Author)

University of Connecticut ( email )

Donggyu Kim

College of Business, Korea Advanced Institute of Science and Technology (KAIST) ( email )

85 Hoegiro Dongdaemun-Gu
Seoul 02455
Korea, Republic of (South Korea)

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