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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>.
Getting started
Browse package contents
Package details |
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| Author | Daniel P. Palomar [cre, aut], Rui Zhou [aut], Xiwen Wang [aut] |
| Maintainer | Daniel P. Palomar <daniel.p.palomar@gmail.com> |
| License | GPL-3 |
| Version | 0.1.1 |
| URL | https://github.com/dppalomar/highOrderPortfolios https://www.danielppalomar.com |
| Package repository | View on CRAN |
| Installation |
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