| highOrderPortfolios-package {highOrderPortfolios} | R Documentation |
highOrderPortfolios: Design of High-Order Portfolios via Mean, Variance, Skewness, and Kurtosis
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.
Functions
design_MVSK_portfolio_via_sample_moments(), design_MVSK_portfolio_via_skew_t(),
and design_MVSKtilting_portfolio_via_sample_moments()
Help
For a quick help see the README file: GitHub-README.
Author(s)
Rui Zhou, Xiwen Wang, and Daniel P. Palomar
References
R. Zhou and D. P. Palomar, "Solving High-Order Portfolios via Successive Convex Approximation Algorithms," in IEEE Transactions on Signal Processing, vol. 69, pp. 892-904, 2021. <https://doi.org/10.1109/TSP.2021.3051369>.
X. Wang, R. Zhou, J. Ying, and D. P. Palomar, "Efficient and Scalable High-Order Portfolios Design via Parametric Skew-t Distribution," Available in arXiv, 2022. <https://arxiv.org/pdf/2206.02412.pdf>.