The paper describes two algorithms for financial portfolio optimization with the following risk measures: CVaR, MAD, LSAD and dispersion CVaR. These algorithms can be applied to discrete distributions of asset returns since then the optimization problems can be reduced to linear programs. The first algorithm solves a simple recourse problem as described by Haneveld using Benders de composition method. The second algorithm finds an optimal portfolio with the smallest distance to a given benchmark portfolio and is an adaptation of the least norm solution (called also normal solution) of linear programs due to Zhao and Li. The algorithms are implemented in R in the package PortfolioOptim.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2018-028.zip
fPortfolio, PortfolioAnalytics, Rglpk, quadprog, DEoptim, GenSA, psoptim, parma, nloptr, PortfolioOptim
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For attribution, please cite this work as
Palczewski, "LP Algorithms for Portfolio Optimization: The PortfolioOptim Package", The R Journal, 2018
BibTeX citation
@article{RJ-2018-028,
author = {Palczewski, Andrzej},
title = {LP Algorithms for Portfolio Optimization: The PortfolioOptim Package},
journal = {The R Journal},
year = {2018},
note = {https://doi.org/10.32614/RJ-2018-028},
doi = {10.32614/RJ-2018-028},
volume = {10},
issue = {1},
issn = {2073-4859},
pages = {308-327}
}