Forecasting High-Dimensional Covariance Matrices Using High-Dimensional Principal Component Analysis

We modify the recently proposed forecasting model of high-dimensional covariance matrices (HDCM) of asset returns using high-dimensional principal component analysis (PCA). It is well-known that when the sample size is smaller than the dimension, eigenvalues estimated by classical PCA have a bias. I...

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Autors principals:	Hideto Shigemoto, Takayuki Morimoto
Format:	Artigo
Idioma:	Inglês
Publicat:	MDPI AG 2022-12-01
Col·lecció:	Axioms
Matèries:	covariance forecasting high-dimensional covariance high-frequency data principal component analysis time series
Accés en línia:	https://www.mdpi.com/2075-1680/11/12/692
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