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>, Bodnar et al. (2019) <doi:10.1109/TSP.2019.2929964>, Bodnar et al. (2020) <doi:10.1109/TSP.2020.3037369> 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: 0.1.2
Depends: R (≥ 3.5.0)
Imports: Rdpack
Suggests: ggplot2, testthat, EstimDiagnostics, MASS, corpcor, waldo
Published: 2021-10-23
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>
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:

Reference manual: HDShOP.pdf

Downloads:

Package source: HDShOP_0.1.2.tar.gz
Windows binaries: r-devel: HDShOP_0.1.1.zip, r-release: HDShOP_0.1.1.zip, r-oldrel: HDShOP_0.1.1.zip
macOS binaries: r-release (arm64): HDShOP_0.1.1.tgz, r-release (x86_64): HDShOP_0.1.1.tgz, r-oldrel: HDShOP_0.1.1.tgz
Old sources: HDShOP archive

Reverse dependencies:

Reverse suggests: DOSPortfolio

Linking:

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