HDShOP

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.