mean_js

James-Stein shrinkage estimator of the mean vector as suggested in
Jorion1986;textualHDShOP. The estimator is given by
$$\hat \mu_{JS} = (1-\beta) \bar x + \beta Y_0 1,$$
where $\bar x$ is the sample mean vector, $\beta$ is the shrinkage
coefficient which minimizes a quadratic loss given by Eq.(11) in
Jorion1986;textualHDShOP.
$Y_0$ is a prespecified value.

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_js 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>Y_0</dt>
<dd>a numeric variable. Shrinkage target coefficient.</dd></dl>

Arguments

James-Stein shrinkage estimator of the mean vector — mean_js

<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>Y_0</dt>
<dd>a numeric variable. Shrinkage target coefficient.</dd>

</dl>

mean_js: James-Stein shrinkage estimator of the mean vector

Description

Usage

Value

Arguments

References

Examples