CRAN/E | highOrderPortfolios

highOrderPortfolios

Design of High-Order Portfolios Including Skewness and Kurtosis

Installation

About

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." .

Citation highOrderPortfolios citation info
github.com/dppalomar/highOrderPortfolios
www.danielppalomar.com
Bug report File report

Key Metrics

Version 0.1.1
R ≥ 3.5.0
Published 2022-10-20 526 days ago
Needs compilation? yes
License GPL-3
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Maintainer

Maintainer

Daniel P. Palomar

daniel.p.palomar@gmail.com

Authors

Daniel P. Palomar

cre / aut

Rui Zhou

aut

Xiwen Wang

aut

Material

README
NEWS
Reference manual
Package source

Vignettes

Design of High-order Portfolios

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-develnot available

x86_64

r-releasenot available

x86_64

r-oldrelnot available

x86_64

Old Sources

highOrderPortfolios archive

Depends

R ≥ 3.5.0

Imports

ECOSolveR
lpSolveAPI
nloptr
PerformanceAnalytics
quadprog
fitHeavyTail ≥ 0.1.4
stats
utils

Suggests

knitr
ggplot2
rmarkdown
R.rsp
testthat ≥ 3.0.0