A Unified Framework for Estimation of High-Dimensional Conditional Factor Models

• 成果类型: Article
• 署名作者: Chen, Qihui
• 署名单位: The Chinese University of Hong Kong, Shenzhen
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2025.2591269
• 发表日期: 2026-04-03
• 页码: 1206-1218
• 关键词: Asset pricing; Characteristics; Constrained nuclear norm regularization; factor zoo; Macro state variables; arbitrage; number

摘要

This article presents a general framework for estimating high-dimensional conditional latent factor models via constrained nuclear norm regularization. We establish large sample properties of the estimators and provide efficient algorithms for their computation. To improve practical applicability, we propose a cross-validation procedure for selecting the regularization parameter. Our framework unifies the estimation of various conditional factor models, enabling the derivation of new asymptotic results while addressing limitations of existing methods, which are often model-specific or restrictive. Empirical analyses of the cross section of individual US stock returns suggest that imposing homogeneity improves the model's out-of-sample predictability, with our new method outperforming existing alternatives. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.