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: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=highOrderPortfolios to link to this page.
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