High-Dimensional Shrinkage Optimal Portfolios
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) .