highOrderPortfolios: Design of High-Order Portfolios Including Skewness and Kurtosis

The classical Markowitz's mean-variance portfolio formulation ignores heavy tails and skewness. High-order portfolios use higher order moments to better characterize the return distribution. Different formulations and fast algorithms are proposed for high-order portfolios based on the mean, variance, skewness, and kurtosis. The package is based on the papers: R. Zhou and D. P. Palomar (2021). "Solving High-Order Portfolios via Successive Convex Approximation Algorithms." <doi:10.48550/arXiv.2008.00863>. X. Wang, R. Zhou, J. Ying, and D. P. Palomar (2022). "Efficient and Scalable High-Order Portfolios Design via Parametric Skew-t Distribution." <doi:10.48550/arXiv.2206.02412>.

Version: 0.1.1
Depends: R (≥ 3.5.0)
Imports: ECOSolveR, lpSolveAPI, nloptr, PerformanceAnalytics, quadprog, fitHeavyTail (≥ 0.1.4), stats, utils
Suggests: knitr, ggplot2, rmarkdown, R.rsp, testthat (≥ 3.0.0)
Published: 2022-10-20
Author: Daniel P. Palomar [cre, aut], Rui Zhou [aut], Xiwen Wang [aut]
Maintainer: Daniel P. Palomar <daniel.p.palomar at gmail.com>
BugReports: https://github.com/dppalomar/highOrderPortfolios/issues
License: GPL-3
URL: https://github.com/dppalomar/highOrderPortfolios, https://www.danielppalomar.com
NeedsCompilation: yes
Citation: highOrderPortfolios citation info
Materials: README NEWS
CRAN checks: highOrderPortfolios results

Documentation:

Reference manual: highOrderPortfolios.pdf
Vignettes: Design of High-order Portfolios

Downloads:

Package source: highOrderPortfolios_0.1.1.tar.gz
Windows binaries: r-devel: highOrderPortfolios_0.1.1.zip, r-release: highOrderPortfolios_0.1.1.zip, r-oldrel: highOrderPortfolios_0.1.1.zip
macOS binaries: r-release (arm64): highOrderPortfolios_0.1.1.tgz, r-oldrel (arm64): highOrderPortfolios_0.1.1.tgz, r-release (x86_64): highOrderPortfolios_0.1.1.tgz
Old sources: highOrderPortfolios archive

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