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-4.7)highOrderPortfolios_0.1.1.zip(r-4.6)highOrderPortfolios_0.1.1.zip(r-4.5)
highOrderPortfolios_0.1.1.tgz(r-4.6-x86_64)highOrderPortfolios_0.1.1.tgz(r-4.6-arm64)highOrderPortfolios_0.1.1.tgz(r-4.5-x86_64)highOrderPortfolios_0.1.1.tgz(r-4.5-arm64)
highOrderPortfolios_0.1.1.tar.gz(r-4.7-arm64)highOrderPortfolios_0.1.1.tar.gz(r-4.7-x86_64)highOrderPortfolios_0.1.1.tar.gz(r-4.6-arm64)highOrderPortfolios_0.1.1.tar.gz(r-4.6-x86_64)
highOrderPortfolios_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
highOrderPortfolios/json (API)
| # 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
Datasets:
Last updated from:f3517955fa. Checks:13 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 202 | ||
| linux-devel-x86_64 | OK | 218 | ||
| source / vignettes | OK | 228 | ||
| linux-release-arm64 | OK | 175 | ||
| linux-release-x86_64 | OK | 191 | ||
| macos-release-arm64 | OK | 135 | ||
| macos-release-x86_64 | OK | 555 | ||
| macos-oldrel-arm64 | OK | 326 | ||
| macos-oldrel-x86_64 | OK | 472 | ||
| windows-devel | OK | 216 | ||
| windows-release | OK | 153 | ||
| windows-oldrel | OK | 162 | ||
| wasm-release | OK | 106 |
Exports:design_MVSK_portfolio_via_sample_momentsdesign_MVSK_portfolio_via_skew_tdesign_MVSKtilting_portfolio_via_sample_momentsestimate_sample_momentsestimate_skew_teval_portfolio_moments
Dependencies:cliDBIECOSolveRfitHeavyTailghypICSICSNPlatticelpSolveAPIMASSMatrixminqamitoolsmvtnormnloptrnumDerivPerformanceAnalyticsquadprogRcppRcppArmadillosurveysurvivalxtszoo
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 |
