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HDShOP  

High-Dimensional Shrinkage Optimal Portfolios
View on CRAN: Click here


Download and install HDShOP package within the R console
Install from CRAN:
install.packages("HDShOP")

Install from Github:
library("remotes")
install_github("cran/HDShOP")

Install by package version:
library("remotes")
install_version("HDShOP", "0.1.5")



Attach the package and use:
library("HDShOP")
Maintained by
Dmitry Otryakhin
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-08-02
Latest Update: 2022-11-08
Description:
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 , 2019 , 2020 , 2021 ) 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) .
How to cite:
Dmitry Otryakhin (2021). HDShOP: High-Dimensional Shrinkage Optimal Portfolios. R package version 0.1.5, https://cran.r-project.org/web/packages/HDShOP. Accessed 22 Dec. 2024.
Previous versions and publish date:
0.1.0 (2021-08-02 10:50), 0.1.1 (2021-09-03 02:20), 0.1.2 (2021-10-23 10:40), 0.1.3 (2022-11-08 11:20)
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