nonlin_shrinkLW

The nonlinear shrinkage estimator of the covariance matrix, that minimizes
the minimum variance loss functions as defined in Eq (2.1) of
LW2020;textualHDShOP.

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

nonlin_shrinkLW 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></dl>

Arguments

nonlinear shrinkage estimator of the covariance matrix of
Ledoit and Wolf (2020) — nonlin_shrinkLW

<dl>

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

</dl>

nonlin_shrinkLW: nonlinear shrinkage estimator of the covariance matrix of Ledoit and Wolf (2020)

Description

Usage

Value

Arguments

References

Examples