HDShOP

HDShOP-package

Package HDShOP has the following three important functions:
<code>MVShrinkPortfolio</code>, <code>CovarEstim</code> and
<code>MeanEstim</code>. MVShrinkPortfolio creates mean-variance
portfolios using shrinkage estimation methods for portfolio weights.
CovarEstim computes several estimators of the covariance matrix, while
MeanEstim computes several estimators of the mean vector. Each of these
three functions is supplied a name of the method used to perform the
estimation. All portfolios are stored in objects of class MeanVar_portfolio
and some have a subclass, specific to their kind, that inherits from
MeanVar_portfolio. For the latter class constructor, validator and
helper functions are available, so that custom mean-variance portfolios
may be coded by users.

internal

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>.

Dmitry Otryakhin

High-Dimensional Shrinkage Optimal Portfolios

Taras Bodnar

Solomiia Dmytriv

Yarema Okhrin

Nestor Parolya

HDShOP-package function

MeanEstim: BOP2019HDShOP, James-Stein and Bayes-Stein
estimators Jorion1986HDShOP.
CovarEstim: BGP2014HDShOP, LW2020HDShOP.
MVShrinkPortfolio: BDOPS2021HDShOP,
BDPS2019HDShOP.
MeanVar_portfolio.

Methods

Maintainer: Dmitry Otryakhin <a href="/link/d.otryakhin.acad%40protonmail.ch?package=HDShOP&version=0.1.5" data-mini-rdoc="HDShOP::d.otryakhin.acad@protonmail.ch">d.otryakhin.acad@protonmail.ch</a> (<a href="https://orcid.org/0000-0002-4700-7221">ORCID</a>)
Authors:<ul>
<li>Taras Bodnar (<a href="https://orcid.org/0000-0001-7855-8221">ORCID</a>)</li>
<li>Solomiia Dmytriv (<a href="https://orcid.org/0000-0003-1855-3044">ORCID</a>)</li>
<li>Yarema Okhrin (<a href="https://orcid.org/0000-0003-4704-5233">ORCID</a>)</li>
<li>Nestor Parolya (<a href="https://orcid.org/0000-0003-2147-2288">ORCID</a>)</li>
</ul>

Author

A set of tools for shrinkage estimation of mean-variance optimal portfolios — HDShOP-package

MeanEstim: BOP2019HDShOP, James-Stein and Bayes-Stein
estimators Jorion1986HDShOP.
CovarEstim: BGP2014HDShOP, LW2020HDShOP.
MVShrinkPortfolio: BDOPS2021HDShOP,
BDPS2019HDShOP.
MeanVar_portfolio.

Maintainer: Dmitry Otryakhin <a href='mailto:d.otryakhin.acad@protonmail.ch'>d.otryakhin.acad@protonmail.ch</a> (<a href='https://orcid.org/0000-0002-4700-7221'>ORCID</a>)
Authors:<ul>
<li>Taras Bodnar (<a href='https://orcid.org/0000-0001-7855-8221'>ORCID</a>)</li>
<li>Solomiia Dmytriv (<a href='https://orcid.org/0000-0003-1855-3044'>ORCID</a>)</li>
<li>Yarema Okhrin (<a href='https://orcid.org/0000-0003-4704-5233'>ORCID</a>)</li>
<li>Nestor Parolya (<a href='https://orcid.org/0000-0003-2147-2288'>ORCID</a>)</li>
</ul>

HDShOP-package: A set of tools for shrinkage estimation of mean-variance optimal portfolios

Description

Arguments

Methods

Author

See Also