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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Team
Daniel P. Palomar
Rui Zhou
Show author detailsRolesAuthorXiwen Wang
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Last 30 days
This package has been downloaded 470 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 3 times.
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Last 365 days
This package has been downloaded 7,587 times in the last 365 days. Impressive! The kind of number that makes colleagues ask, 'How did you do it?' The day with the most downloads was Jul 23, 2024 with 69 downloads.
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- Imports6 packages
- Suggests5 packages