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

Qihui Chen

Papers from arXiv.org

Abstract: This paper 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.

Date: 2022-09, Revised 2025-12
New Economics Papers: this item is included in nep-ecm and nep-ets
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