HDShOP-package: A set of tools for shrinkage estimation of mean-variance...
In HDShOP: High-Dimensional Shrinkage Optimal Portfolios
HDShOP-package R Documentation
A set of tools for shrinkage estimation of mean-variance optimal portfolios
Description
Package HDShOP has the following three important functions: MVShrinkPortfolio,
CovarEstim and MeanEstim. 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.
Methods
MeanEstim: \insertCiteBOP2019HDShOP, James-Stein and Bayes-Stein estimators \insertCiteJorion1986HDShOP.
CovarEstim: \insertCiteBGP2014HDShOP, \insertCiteLW2020HDShOP.
MVShrinkPortfolio: \insertCiteBDOPS2021HDShOP, \insertCiteBDPS2019HDShOP.
References
\insertAllCited
HDShOP documentation built on Nov. 10, 2022, 5:12 p.m.
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| HDShOP-package | R Documentation |
A set of tools for shrinkage estimation of mean-variance optimal portfolios
Description
Package HDShOP has the following three important functions: MVShrinkPortfolio,
CovarEstim and MeanEstim. 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.
Methods
MeanEstim: \insertCiteBOP2019HDShOP, James-Stein and Bayes-Stein estimators \insertCiteJorion1986HDShOP.
CovarEstim: \insertCiteBGP2014HDShOP, \insertCiteLW2020HDShOP.
MVShrinkPortfolio: \insertCiteBDOPS2021HDShOP, \insertCiteBDPS2019HDShOP.
References
\insertAllCitedR Package Documentation
Browse R Packages
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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