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A0455
Title: Large volatility matrix analysis using global and national factor models Authors:  Sung Hoon Choi - University of Connecticut (United States) [presenting]
Donggyu Kim - KAIST (Korea, South)
Abstract: Several large volatility matrix inference procedures have been developed, based on the latent factor model. They often assumed that there are a few common factors, which can account for volatility dynamics. However, previous research demonstrated that there are local factors. Especially, when analyzing the global stock market, we often observe that national-specific factors explain their own volatility dynamics. To account for this, we propose a Double Principal Orthogonal complEment Thresholding (Double-POET) method, based on multi-level factor models. We 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 Double-POET estimators in an out-of-sample portfolio allocation study using international stocks from 13 financial markets.