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: 0.1.5
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 ORCID iD [aut], Solomiia Dmytriv ORCID iD [aut], Yarema Okhrin ORCID iD [aut], Dmitry Otryakhin ORCID iD [aut, cre], Nestor Parolya ORCID iD [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
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 dependencies:

Reverse suggests: DOSPortfolio


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