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LARGE GLOBAL VOLATILITY MATRIX ANALYSIS BASED ON OBSERVATION STRUCTURAL INFORMATION

Published online by Cambridge University Press:  08 November 2024

Sung Hoon Choi Open the ORCID record for Sung Hoon Choi [Opens in a new window]  and
Donggyu Kim Open the ORCID record for Donggyu Kim [Opens in a new window]
Sung Hoon Choi*
Affiliation:
University of Connecticut
Donggyu Kim
Affiliation:
University of California, Riverside
*
Address correspondence to Sung Hoon Choi, Department of Economics, University of Connecticut, Storrs, CT, USA; e-mail: [email protected].
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Abstract

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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.

Type
ARTICLES
Information
Econometric Theory , First View , pp. 1 - 16
Copyright
© The Author(s), 2024. Published by Cambridge University Press

References

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