Package: highOrderPortfolios 0.1.1
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." <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." <arxiv:2206.02412>.
Authors:
highOrderPortfolios_0.1.1.tar.gz
highOrderPortfolios_0.1.1.zip(r-4.5)highOrderPortfolios_0.1.1.zip(r-4.4)highOrderPortfolios_0.1.1.zip(r-4.3)
highOrderPortfolios_0.1.1.tgz(r-4.4-x86_64)highOrderPortfolios_0.1.1.tgz(r-4.4-arm64)highOrderPortfolios_0.1.1.tgz(r-4.3-x86_64)highOrderPortfolios_0.1.1.tgz(r-4.3-arm64)
highOrderPortfolios_0.1.1.tar.gz(r-4.5-noble)highOrderPortfolios_0.1.1.tar.gz(r-4.4-noble)
highOrderPortfolios_0.1.1.tgz(r-4.4-emscripten)highOrderPortfolios_0.1.1.tgz(r-4.3-emscripten)
highOrderPortfolios.pdf |highOrderPortfolios.html✨
highOrderPortfolios/json (API)
NEWS
| # Install 'highOrderPortfolios' in R: |
| install.packages('highOrderPortfolios', repos = c('https://dppalomar.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/dppalomar/highorderportfolios/issues
Last updated 2 years agofrom:f3517955fa. Checks:OK: 9. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 07 2024 |
| R-4.5-win-x86_64 | OK | Nov 07 2024 |
| R-4.5-linux-x86_64 | OK | Nov 07 2024 |
| R-4.4-win-x86_64 | OK | Nov 07 2024 |
| R-4.4-mac-x86_64 | OK | Nov 07 2024 |
| R-4.4-mac-aarch64 | OK | Nov 07 2024 |
| R-4.3-win-x86_64 | OK | Nov 07 2024 |
| R-4.3-mac-x86_64 | OK | Nov 07 2024 |
| R-4.3-mac-aarch64 | OK | Nov 07 2024 |
Exports:design_MVSK_portfolio_via_sample_momentsdesign_MVSK_portfolio_via_skew_tdesign_MVSKtilting_portfolio_via_sample_momentsestimate_sample_momentsestimate_skew_teval_portfolio_moments
Dependencies:DBIECOSolveRfitHeavyTailghypICSICSNPlatticelpSolveAPIMASSMatrixminqamitoolsmvtnormnloptrnumDerivPerformanceAnalyticsquadprogRcppRcppArmadillosurveysurvivalxtszoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| highOrderPortfolios: Design of High-Order Portfolios via Mean, Variance, Skewness, and Kurtosis | highOrderPortfolios-package |
| Design high-order portfolio based on weighted linear combination of first four moments | design_MVSK_portfolio_via_sample_moments |
| Design MVSK portfolio without shorting based on the parameters of generalized hyperbolic skew-t distribution | design_MVSK_portfolio_via_skew_t |
| Design high-order portfolio by tilting a given portfolio to the MVSK efficient frontier | design_MVSKtilting_portfolio_via_sample_moments |
| Estimate first four moment parameters of multivariate observations | estimate_sample_moments |
| Estimate the parameters of skew-t distribution from multivariate observations | estimate_skew_t |
| Evaluate first four moments of a given portfolio | eval_portfolio_moments |
| Synthetic 500x100 matrix dataset | X100 |
| Synthetic 1000x200 matrix dataset | X200 |
| Synthetic 250x50 matrix dataset | X50 |
