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." 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.
- Version0.1.1
- R versionunknown
- LicenseGPL-3
- Needs compilation?Yes
- highOrderPortfolios citation info
- Last release10/20/2022
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Daniel P. Palomar
Rui Zhou
Show author detailsRolesAuthorXiwen Wang
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- Imports6 packages
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