mean_bop19

Shrinkage estimator of the high-dimensional mean vector as suggested in
BOP2019;textualHDShOP. It uses the formula
$$\hat \mu_{BOP} = \hat \alpha \bar x + \hat \beta \mu_0,$$ where
$\hat \alpha$ and $\hat \beta$ are shrinkage coefficients given by
Eq.(6) and Eg.(7) of BOP2019;textualHDShOP that minimize
weighted quadratic loss for a given target vector $\mu_0$
(shrinkage target). $\bar x$ stands for the sample mean vector.

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

HDShOP

High-Dimensional Shrinkage Optimal Portfolios

Taras Bodnar

Solomiia Dmytriv

Yarema Okhrin

Nestor Parolya

mean_bop19 function

<dl><dt>x</dt>
<dd>a p by n matrix or a data frame of asset returns. Rows represent
different assets, columns -- observations.</dd>
<dt>mu_0</dt>
<dd>a numeric vector. The target vector used in the construction of
the shrinkage estimator.</dd></dl>

Arguments

BOP shrinkage estimator — mean_bop19

<dl>

<dt>x</dt>
<dd>a p by n matrix or a data frame of asset returns. Rows represent
different assets, columns -- observations.</dd>


<dt>mu_0</dt>
<dd>a numeric vector. The target vector used in the construction of
the shrinkage estimator.</dd>

</dl>

mean_bop19: BOP shrinkage estimator

Description

Usage

Value

Arguments

References

Examples