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) , Bodnar et al. (2019) , Bodnar et al. (2020) 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) .


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install.packages("HDShOP")

0.1.1 by Dmitry Otryakhin, 21 days ago


https://github.com/Otryakhin-Dmitry/global-minimum-variance-portfolio


Browse source code at https://github.com/cran/HDShOP


Authors: Taras Bodnar [aut] , Solomiia Dmytriv [aut] , Yarema Okhrin [aut] , Dmitry Otryakhin [aut, cre] , Nestor Parolya [aut]


Documentation:   PDF Manual  


GPL-3 license


Imports Rdpack

Suggests ggplot2, testthat, EstimDiagnostics, MASS, corpcor, waldo


Suggested by DOSPortfolio.


See at CRAN