Maurizio Daniele, Winfried Pohlmeier, Aygul Zagidullina
arXiv 13 Jun 2019 · Econometrics · 3 citations (OpenAlex)
arXiv:1906.05545 · PDF · DOI · OpenAlex · Extracted main text
We propose a novel estimation approach for the covariance matrix based on the $l_1$-regularized approximate factor model. Our sparse approximate factor (SAF) covariance estimator allows for the existence of weak factors and hence relaxes the pervasiveness assumption generally adopted for the standard approximate factor model. We prove consistency of the covariance matrix estimator under the Frobenius norm as well as the consistency of the factor loadings and the factors. Our Monte Carlo simulations reveal that the SAF covariance estimator has superior properties in finite samples for low and high dimensions and different designs of the covariance matrix. Moreover, in an out-of-sample portfolio forecasting application the estimator uniformly outperforms alternative portfolio strategies based on alternative covariance estimation approaches and modeling strategies including the $1/N$-strategy.
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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 | Bickel and Levina (2008) Covariance regularization by thresholding | 1.000 | 5 | 3 | 100% |
| 2 | Fan, Liao, and Mincheva (2013) Large covariance estimation by thresholding principal orthogonal complements | 0.865 | 17 | 8 | 65% |
| AbadirDistasoZikes2014 | unmatched citation key AbadirDistasoZikes2014 | 0.794 | 8 | 4 | 50% |
| 4 | Bai and Liao (2016) Efficient estimation of approximate factor models via penalized maximum likelihood | 0.794 | 6 | 3 | 50% |
| 5 | Kourtis, Dotsis, and Markellos (2012) Parameter uncertainty in portfolio selection: Shrinking the inverse covariance matrix | 0.754 | 7 | 4 | 43% |
| 6 | Ledoit and Wolf (2003) Improved estimation of the covariance matrix of stock returns with an application to portfolio selection | 0.754 | 7 | 4 | 43% |
| 7 | Bien and Tibshirani (2011) Sparse estimation of a covariance matrix | 0.737 | 10 | 5 | 40% |
| 8 | Fama and French (1993) Common risk factors in the returns on stocks and bonds | 0.737 | 4 | 4 | 50% |
| 9 | Fan, Fan, and Lv (2008) High dimensional covariance matrix estimation using a factor model | 0.737 | 3 | 3 | 67% |
| 10 | Ledoit and Wolf (2018) Optimal estimation of a large-dimensional covariance matrix under Stein's loss | 0.721 | 8 | 4 | 38% |
Showing the top 10 of 42 scored citations. 1 of these could not be matched to a bibliography entry, so only the citation key is shown.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Sequential Estimation of Multivariate Factor Stochastic Volatility Models | 0.405 | 1 | 1 |