HDShOP: High-Dimensional Shrinkage Optimal Portfolios
Constructs shrinkage estimators of high-dimensional mean-variance portfolios and performs high-dimensional tests on optimality of a given portfolio. The techniques developed in Bodnar et al. (2018 <doi:10.1016/j.ejor.2017.09.028>, 2019 <doi:10.1109/TSP.2019.2929964>, 2020 <doi:10.1109/TSP.2020.3037369>, 2021 <doi:10.1080/07350015.2021.2004897>) are central to the package. They provide simple and feasible estimators and tests for optimal portfolio weights, which are applicable for 'large p and large n' situations where p is the portfolio dimension (number of stocks) and n is the sample size. The package also includes tools for constructing portfolios based on shrinkage estimators of the mean vector and covariance matrix as well as a new Bayesian estimator for the Markowitz efficient frontier recently developed by Bauder et al. (2021) <doi:10.1080/14697688.2020.1748214>.
| Version: | 0.1.7 |
| Depends: | R (≥ 3.5.0) |
| Imports: | Rdpack,lattice |
| Suggests: | ggplot2,testthat (≥ 3.0.0),EstimDiagnostics,MASS,corpcor,waldo |
| Published: | 2025-11-14 |
| DOI: | 10.32614/CRAN.package.HDShOP |
| Author: | Taras Bodnar [aut], Solomiia Dmytriv [aut], Yarema Okhrin [aut], Dmitry Otryakhin [aut, cre], Nestor Parolya [aut] |
| Maintainer: | Dmitry Otryakhin <d.otryakhin.acad at protonmail.ch> |
| BugReports: | https://github.com/Otryakhin-Dmitry/global-minimum-variance-portfolio/issues |
| License: | GPL-3 |
| URL: | https://github.com/Otryakhin-Dmitry/global-minimum-variance-portfolio |
| NeedsCompilation: | no |
| Citation: | HDShOP citation info |
| Materials: | NEWS |
| In views: | Finance |
| CRAN checks: | HDShOP results |
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