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>.

Package details

AuthorTaras Bodnar [aut] (<https://orcid.org/0000-0001-7855-8221>), Solomiia Dmytriv [aut] (<https://orcid.org/0000-0003-1855-3044>), Yarema Okhrin [aut] (<https://orcid.org/0000-0003-4704-5233>), Dmitry Otryakhin [aut, cre] (<https://orcid.org/0000-0002-4700-7221>), Nestor Parolya [aut] (<https://orcid.org/0000-0003-2147-2288>)
MaintainerDmitry Otryakhin <d.otryakhin.acad@protonmail.ch>
URL https://github.com/Otryakhin-Dmitry/global-minimum-variance-portfolio
Package repositoryView on CRAN
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HDShOP documentation built on Oct. 23, 2021, 9:06 a.m.