A robust latent factor model for high-dimensional portfolio selection

Fangquan Shi, Lianjie Shu and Xinhua Gu

Journal of Empirical Finance, 2025, vol. 83, issue C

Abstract: Portfolio selection, faced with large volatile data sets of strongly correlated asset returns, is prone to unstable portfolio weights and serious estimation error. To attenuate this problem, our work proposes a new latent factor model equipped with both a suitable robust estimator to deal with cellwise data contamination and a diagonally-dominant (DD) covariance structure to account for cross-sectional dependence among residual returns. The proposed robust DD model is found to compare favorably with various competitors from the literature in terms of out-of-sample portfolio performance across real-world data sets.

Keywords: Portfolio optimization; Latent factors; High dimensions; Robust estimation; Diagonal dominance (search for similar items in EconPapers)
JEL-codes: C13 C58 G11 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0927539825000453
Full text for ScienceDirect subscribers only

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:eee:empfin:v:83:y:2025:i:c:s0927539825000453

DOI: 10.1016/j.jempfin.2025.101623

Access Statistics for this article

Journal of Empirical Finance is currently edited by R. T. Baillie, F. C. Palm, Th. J. Vermaelen and C. C. P. Wolff

More articles in Journal of Empirical Finance from Elsevier
Bibliographic data for series maintained by Catherine Liu ().