arXiv 1 Sep 2022 · Econometrics · 7 citations (OpenAlex)
arXiv:2209.00391 · PDF · DOI · OpenAlex · Extracted main text
This paper develops a general framework for estimation of high-dimensional conditional factor models via nuclear norm regularization. We establish large sample properties of the estimators, and provide an efficient computing algorithm for finding the estimators as well as a cross validation procedure for choosing the regularization parameter. The general framework allows us to estimate a variety of conditional factor models in a unified way and quickly deliver new asymptotic results. We apply the method to analyze the cross section of individual US stock returns, and find that imposing homogeneity may improve the model's out-of-sample predictability.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Gagliardini, P., E. Ossola, and O. Scaillet (2016) Time-varying risk premium in large cross‐sectional equity data sets | 1.000 | 6 | 3 | 100% |
| 2 | Chen, Q., N. Roussanov, and X. Wang (2021) Semiparametric Conditional Factor Models in Asset Pricing, Tech self | 0.928 | 15 | 6 | 80% |
| 3 | Pelger, M. and R. Xiong (2022) State-varying factor models of large dimensions | 0.928 | 4 | 3 | 100% |
| 4 | Fan, J., Y. Liao, and W. Wang (2016) Projected principal component analysis in factor models | 0.920 | 9 | 4 | 78% |
| 5 | Negahban, S. and M. J. Wainwright (2011) Estimation of (near) low-rank matrices with noise and high-dimensional scaling | 0.874 | 6 | 5 | 67% |
| 6 | Kelly, B. T., S. Pruitt, and Y. Su (2019) Characteristics are covariances: A unified model of risk and return | 0.874 | 5 | 2 | 100% |
| 7 | Ma, S., D. Goldfarb, and L. Chen (2011) Fixed point and Bregman iterative methods for matrix rank minimization | 0.737 | 3 | 3 | 67% |
| 8 | Connor, G., M. Hagmann, and O. Linton (2012) Efficient semiparametric estimation of the Fama–French model and extensions | 0.737 | 3 | 2 | 100% |
| 9 | Kim, S., R. A. Korajczyk, and A. Neuhierl (2021) Arbitrage portfolios | 0.737 | 3 | 2 | 100% |
| 10 | Moon, H. R. and M. Weidner (2023) Nuclear Norm Regularized Estimation of Panel Regression Models, Tech | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 51 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Nuclear Norm Regularized Estimation of Panel Regression Models | 0.511 | 2 | 1 |
| 2 | Target PCA: Transfer Learning Large Dimensional Panel Data | 0.405 | 1 | 1 |