To cite 'highOrderPortfolios' in publications, please use:

R. Zhou, X. Wang, and D. P. Palomar (2022). highOrderPortfolios: Design of High-Order Portfolios via Mean, Variance, Skewness, and Kurtosis. R package version 0.1.0. https://CRAN.R-project.org/package=highOrderPortfolios

R. Zhou and D. P. Palomar (2021). Solving High-Order Portfolios via Successive Convex Approximation Algorithms. IEEE Transactions on Signal Processing, vol. 69 pp. 892-904. https://doi.org/10.1109/TSP.2021.3051369

X. Wang, R. Zhou, J. Ying, and D. P. Palomar (2022). Efficient and Scalable High-Order Portfolios Design via Parametric Skew-t Distribution. Available in arXiv, https://arxiv.org/pdf/2206.02412.pdf

Corresponding BibTeX entries:

  @Manual{,
    title = {{highOrderPortfolios: Design of High-Order Portfolios via
      Mean, Variance, Skewness, and Kurtosis}},
    author = {R. Zhou and X. Wang and D. P. Palomar},
    note = {R package version 0.1.0},
    year = {2022},
    url = {https://CRAN.R-project.org/package=highOrderPortfolios},
  }

  @Article{,
    title = {Solving High-Order Portfolios via Successive Convex
      Approximation Algorithms},
    author = {Rui Zhou and Daniel P. Palomar},
    journal = {IEEE Transactions on Signal Processing},
    volume = {69},
    pages = {892-904},
    year = {2021},
    url = {https://doi.org/10.1109/TSP.2021.3051369},
  }

  @Article{,
    title = {Efficient and Scalable High-Order Portfolios Design via
      Parametric Skew-t Distribution},
    author = {Xiwen Wang and Rui Zhou and Jiaxi Ying and Daniel P.
      Palomar},
    journal = {Available in arXiv},
    year = {2022},
    url = {https://arxiv.org/pdf/2206.02412.pdf},
  }