Large Volatility Matrix Analysis Using Global and National Factor Models

63 Pages Posted: 9 Sep 2022 Last revised: 2 Mar 2023

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: August 25, 2022

Abstract

Several large volatility matrix inference procedures have been developed, based on the latent factor model. They often assumed that there are a few of common factors, which can account for volatility dynamics. However, several studies have demonstrated the presence of local factors. In particular, when analyzing the global stock market, we often observe that nation-specific factors explain their own country's volatility dynamics. To account for this, we propose the Double Principal Orthogonal complEment Thresholding (Double-POET) method, based on multi-level factor models, and also establish its asymptotic properties. Furthermore, we demonstrate the drawback of using the regular principal orthogonal component thresholding (POET) when the local factor structure exists. We also describe the blessing of dimensionality using Double-POET for local covariance matrix estimation. Finally, we investigate the performance of the Double-POET estimator in an out-of-sample portfolio allocation study using international stocks from 20 financial markets.

Keywords: High-dimensionality, low-rank matrix, multi-level factor model, POET, sparsity

JEL Classification: C38, C58

Suggested Citation

Choi, Sung Hoon and Kim, Donggyu, Large Volatility Matrix Analysis Using Global and National Factor Models (August 25, 2022). KAIST College of Business Working Paper Series, Available at SSRN: https://ssrn.com/abstract=4200781 or http://dx.doi.org/10.2139/ssrn.4200781

Sung Hoon Choi

University of Connecticut ( email )

Donggyu Kim (Contact Author)

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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