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.

Last 30 days

This package has been downloaded 241 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 6 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 3,395 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Jan 30, 2025 with 41 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.