Package: HDShOP 0.1.5

HDShOP: High-Dimensional Shrinkage Optimal Portfolios

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>.

Authors:Taras Bodnar [aut], Solomiia Dmytriv [aut], Yarema Okhrin [aut], Dmitry Otryakhin [aut, cre], Nestor Parolya [aut]

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Bug tracker:https://github.com/otryakhin-dmitry/global-minimum-variance-portfolio/issues

On CRAN:

financial-mathematicshigh-dimensional-dataportfolio-managementshrinkage-estimators

22 exports 5 stars 1.53 score 3 dependencies 7 scripts 258 downloads

Last updated 6 months agofrom:ef1b7a9e3b. Checks:OK: 7. Indexed: yes.

Exports:CovarEstimCovShrinkBGP14InvCovShrinkBGP16mean_bop19mean_bsmean_jsMeanEstimMeanVar_portfolioMVShrinkPortfolionew_GMV_portfolio_weights_BDPS19new_GMV_portfolio_weights_BDPS19_pgnnew_MeanVar_portfolionew_MV_portfolio_traditionalnew_MV_portfolio_traditional_pgnnew_MV_portfolio_weights_BDOPS21new_MV_portfolio_weights_BDOPS21_pgnnonlin_shrinkLWplot_frontierRandCovMtrxSigma_sample_estimatortest_MVSPvalidate_MeanVar_portfolio

Dependencies:latticerbibutilsRdpack