Estimating latent asset-pricing factors

DOI10.1016/j.jeconom.2019.08.012zbMath1456.62252OpenAlexW3023737942MaRDI QIDQ2190237

Martin Lettau, Markus Pelger

Publication date: 18 June 2020

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jeconom.2019.08.012

zbMATH Keywords

high-dimensional datalatent factorsPCAanomaliesweak factorscross section of returnsexpected returns

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Applications of statistics to actuarial sciences and financial mathematics (62P05)

Related Items (11)

Factor models with many assets: strong factors, weak factors, and the two-pass procedure ⋮ High-dimensional latent panel quantile regression with an application to asset pricing ⋮ Are bond returns predictable with real-time macro data? ⋮ Approximate factor models with weaker loadings ⋮ Mining the factor zoo: estimation of latent factor models with sufficient proxies ⋮ Identifying latent factors based on high-frequency data ⋮ Large dimensional latent factor modeling with missing observations and applications to causal inference ⋮ A Simple Method for Predicting Covariance Matrices of Financial Returns ⋮ High-dimensional Statistical Arbitrage with Factor Models and Stochastic Control ⋮ Rank regularized estimation of approximate factor models ⋮ Edge statistics of large dimensional deformed rectangular matrices

Uses Software

• OptShrink

Cites Work

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• Asymptotics of the principal components estimator of large factor models with weakly influential factors
• The eigenvalues and eigenvectors of finite, low rank perturbations of large random matrices
• Spectral analysis of large dimensional random matrices
• Using principal component analysis to estimate a high dimensional factor model with high-frequency data
• Large-dimensional factor modeling based on high-frequency observations
• Regularized estimation of large covariance matrices
• Eigenvalue Ratio Test for the Number of Factors
• OptShrink: An Algorithm for Improved Low-Rank Signal Matrix Denoising by Optimal, Data-Driven Singular Value Shrinkage
• Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets
• Large Sample Covariance Matrices and High-Dimensional Data Analysis
• Inferential Theory for Factor Models of Large Dimensions
• Determining the Number of Factors in Approximate Factor Models
• Large Covariance Estimation by Thresholding Principal Orthogonal Complements
• Projected principal component analysis in factor models

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