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 [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 Materials: NEWS In views: Finance CRAN checks: HDShOP results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=HDShOP to link to this page.
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