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 |
Documentation:
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