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 |
Documentation:
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