High-Dimensional Shrinkage Optimal Portfolios
S3 class MeanVar_portfolio
Covariance matrix estimator
Linear shrinkage estimator of the covariance matrix if(!exists(".Rdpac...
A set of tools for shrinkage estimation of mean-variance optimal portf...
Linear shrinkage estimator of the inverse covariance matrix if(!exists...
BOP shrinkage estimator
Bayes-Stein shrinkage estimator of the mean vector
James-Stein shrinkage estimator of the mean vector
Mean vector estimator
A helper function for MeanVar_portfolio
Shrinkage mean-variance portfolio
Constructor of GMV portfolio object.
A constructor for class MeanVar_portfolio
Traditional mean-variance portfolio
Constructor of MV portfolio object
nonlinear shrinkage estimator of the covariance matrix of Ledoit and W...
Plot the Bayesian efficient frontier if(!exists(".Rdpack.currefs")) .R...
Covariance matrix generator
Sample covariance matrix
Test for mean-variance portfolio weights
A validator for objects of class MeanVar_portfolio
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>.
Useful links