Regularized covariance matrix estimation in high dimensional approximate factor models

Jing Zhang and Shaojun Guo

Statistics & Probability Letters, 2024, vol. 207, issue C

Abstract: We propose a novel factor-based regularized covariance matrix estimator when the number of factors is large compared to the sample size and derive the convergence rates of our estimator. Empirical results demonstrate our proposed estimator performs well in finite samples.

Keywords: High dimensionality; Factor model; Lasso; Adaptive thresholding; Entropy loss (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.spl.2023.110017

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