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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: | 0.1.5 |
Depends: | R (≥ 3.5.0) |
Imports: | Rdpack, lattice |
Suggests: | ggplot2, testthat, EstimDiagnostics, MASS, corpcor, waldo |
Published: | 2024-03-25 |
DOI: | 10.32614/CRAN.package.HDShOP |
Author: | Taras Bodnar |
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 |
Materials: | NEWS |
In views: | Finance |
CRAN checks: | HDShOP results |
Reference manual: | HDShOP.pdf |
Package source: | HDShOP_0.1.5.tar.gz |
Windows binaries: | r-devel: HDShOP_0.1.5.zip, r-release: HDShOP_0.1.5.zip, r-oldrel: HDShOP_0.1.5.zip |
macOS binaries: | r-release (arm64): HDShOP_0.1.5.tgz, r-oldrel (arm64): HDShOP_0.1.5.tgz, r-release (x86_64): HDShOP_0.1.5.tgz, r-oldrel (x86_64): HDShOP_0.1.5.tgz |
Old sources: | HDShOP archive |
Reverse suggests: | DOSPortfolio |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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