arXiv 12 Jul 2024 · Econometrics · publishedJournal of Econometrics (2024) · 2 citations (OpenAlex)
arXiv:2407.09696 · PDF · DOI · OpenAlex · Extracted main text
This paper develops a large-scale inference approach for the regularization of stock return covariance matrices. The framework allows for the presence of heavy tails and multivariate GARCH-type effects of unknown form among the stock returns. The approach involves simultaneous testing of all pairwise correlations, followed by setting non-statistically significant elements to zero. This adaptive thresholding is achieved through sign-based Monte Carlo resampling within multiple testing procedures, controlling either the traditional familywise error rate, a generalized familywise error rate, or the false discovery proportion. Subsequent shrinkage ensures that the final covariance matrix estimate is positive definite and well-conditioned while preserving the achieved sparsity. Compared to alternative estimators, this new regularization method demonstrates strong performance in simulation experiments and real portfolio optimization.
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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 | Westfall, P. and S. Young (1993) Resampling-Based Multiple Testing: Examples and Methods for p-Value Adjustment | 1.000 | 7 | 3 | 100% |
| 2 | Romano, J. and M. Wolf (2005) Exact and approximate stepdown methods for multiple hypothesis testing | 0.874 | 6 | 3 | 67% |
| 3 | Ledoit, O. and M. Wolf (2004) A well-conditioned estimator for large-dimensional covariance matrices | 0.843 | 4 | 4 | 75% |
| 4 | Lehmann, E. and J. Romano (2005) Generalizations of the familywise error rate | 0.737 | 3 | 2 | 100% |
| 5 | Ledoit, O. and M. Wolf (2003) Improved estimation of the covariance matrix of stock returns with an application to portfolio selection | 0.644 | 2 | 2 | 100% |
| 6 | De Nard, G., R. Engle, O. Ledoit, and M. Wolf (2022) Large dynamic covariance matrices: Enhancements based on intraday data | 0.585 | 3 | 1 | 100% |
| 7 | Dufour, J.-M (2006) Monte Carlo tests with nuisance parameters: A general approach to finite-sample inference and nonstandard asymptotics in econome… | 0.511 | 3 | 2 | 33% |
| 8 | Randles, R. and D. Wolfe (1979) Introduction to the Theory of Nonparametric Statistics | 0.511 | 3 | 2 | 33% |
| 9 | Ledoit, O. and M. Wolf (2015) Spectrum estimation: A unified framework for covariance matrix estimation and PCA in large dimensions | 0.511 | 2 | 2 | 50% |
| 10 | Ramprasad, P (2016) nlshrink: Non-linear shrinkage estimation of population eigenvalues and covariance matrices | 0.511 | 2 | 2 | 50% |
Showing the top 10 of 60 scored citations.