Package: highOrderPortfolios
Title: Design of High-Order Portfolios Including Skewness and Kurtosis
Version: 0.1.1
Date: 2022-10-20
Description: 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@R: c(
  person(c("Daniel", "P."), "Palomar", role = c("cre", "aut"), email = "daniel.p.palomar@gmail.com"),
  person("Rui", "Zhou", role = "aut", email = "rzhouae@connect.ust.hk"),
  person("Xiwen", "Wang", role = "aut", email = "xwangew@connect.ust.hk")
  )
Maintainer: Daniel P. Palomar <daniel.p.palomar@gmail.com>
URL: https://github.com/dppalomar/highOrderPortfolios,
        https://www.danielppalomar.com
BugReports: https://github.com/dppalomar/highOrderPortfolios/issues
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.2.1
Imports: ECOSolveR, lpSolveAPI, nloptr, PerformanceAnalytics, quadprog,
        fitHeavyTail (>= 0.1.4), stats, utils
Suggests: knitr, ggplot2, rmarkdown, R.rsp, testthat (>= 3.0.0)
VignetteBuilder: knitr, rmarkdown, R.rsp
Config/testthat/edition: 3
Config/pak/sysreqs: cmake make
Repository: https://dppalomar.r-universe.dev
Date/Publication: 2022-11-29 01:03:39 UTC
RemoteUrl: https://github.com/dppalomar/highorderportfolios
RemoteRef: HEAD
RemoteSha: f3517955fa42969440bc4dd94d599dbd2373aa32
NeedsCompilation: yes
Packaged: 2026-06-09 07:07:06 UTC; root
Author: Daniel P. Palomar [cre, aut],
  Rui Zhou [aut],
  Xiwen Wang [aut]
Depends: R (>= 3.5.0)