Sub-Gaussian Estimation of the Scatter Matrix in Ultra-High Dimensional Elliptical Factor Models with 2 + eth Moment

Yi Ding () and Xinghua Zheng ()
Additional contact information
Yi Ding: Faculty of Business Administration, University of Macau
Xinghua Zheng: Department of ISOM, Hong Kong University of Science and Technology

No 202529, Working Papers from University of Macau, Faculty of Business Administration

Abstract: We study the estimation of scatter matrices in elliptical factor models with 2 + eth moment. For such heavy-tailed data, robust estimators like the Hubertype estimator in Fan et al. (2018) cannot achieve a sub-Gaussian convergence rate. In this paper, we develop an idiosyncratic-projected self-normalization method to remove the effect of the heavy-tailed scalar component and propose a robust estimator of the scatter matrix that achieves the sub-Gaussian rate under an ultra-high dimensional setting. Such a high convergence rate leads to superior performance in estimating high-dimensional global minimum variance portfolios.

Keywords: High-dimension; elliptical model; factor model; scatter matrix; robust estimation (search for similar items in EconPapers)
Pages: 32 pages
Date: 2025-06
New Economics Papers: this item is included in nep-ets
References: Add references at CitEc
Citations:

Published in UM-FBA Working Paper Series

Downloads: (external link)
https://fba.um.edu.mo/wp-content/uploads/RePEc/doc/202529.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:boa:wpaper:202529

Access Statistics for this paper

More papers in Working Papers from University of Macau, Faculty of Business Administration Contact information at EDIRC.
Bibliographic data for series maintained by Carla Leong ().