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highOrderPortfolios  

Design of High-Order Portfolios Including Skewness and Kurtosis
View on CRAN: Click here


Download and install highOrderPortfolios package within the R console
Install from CRAN:
install.packages("highOrderPortfolios")

Install from Github:
library("remotes")
install_github("cran/highOrderPortfolios")

Install by package version:
library("remotes")
install_version("highOrderPortfolios", "0.1.1")



Attach the package and use:
library("highOrderPortfolios")
Maintained by
Daniel P. Palomar
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-10-11
Latest Update: 2022-10-20
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. The package is based on the papers: R. Zhou and D. P. Palomar (2021). "Solving High-Order Portfolios via Successive Convex Approximation Algorithms." . X. Wang, R. Zhou, J. Ying, and D. P. Palomar (2022). "Efficient and Scalable High-Order Portfolios Design via Parametric Skew-t Distribution." .
How to cite:
Daniel P. Palomar (2022). highOrderPortfolios: Design of High-Order Portfolios Including Skewness and Kurtosis. R package version 0.1.1, https://cran.r-project.org/web/packages/highOrderPortfolios. Accessed 06 Nov. 2024.
Previous versions and publish date:
0.0.2 (2022-10-11 08:12), 0.1.0 (2022-10-19 09:45)
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