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 |
| DOI: | 10.32614/CRAN.package.highOrderPortfolios |
| 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 |
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