estimate_sample_moments: Estimate first four moment parameters of multivariate...

In highOrderPortfolios: Design of High-Order Portfolios Including Skewness and Kurtosis

Estimate first four moment parameters of multivariate observations

Description

Estimate first four moments of multivariate observations, namely, mean vector, covariance matrix, coskewness matrix, and cokurtosis matrix.

Usage

estimate_sample_moments(X, adjust_magnitude = FALSE)

Arguments

X	Data matrix.
adjust_magnitude	Boolean indicating whether to adjust the order of magnitude of parameters. Note: this is specially designed for the function design_MVSKtilting_portfolio_via_sample_moments().

Value

A list containing the following elements:

mu	Mean vector.
Sgm	Covariance matrix.
Phi_mat	Co-skewness matrix.
Psi_mat	Co-kurtosis matrix.
Phi	Co-skewness matrix in vector form (collecting only the unique elements).
Psi	Co-kurtosis matrix in vector form (collecting only the unique elements).
Phi_shred	Partition on Phi (see reference).
Psi_shred	Partition on Psi (see reference).

Author(s)

Rui Zhou and Daniel P. Palomar

References

R. Zhou and D. P. Palomar, "Solving High-Order Portfolios via Successive Convex Approximation Algorithms," in IEEE Transactions on Signal Processing, vol. 69, pp. 892-904, 2021. <doi:10.1109/TSP.2021.3051369>.

Examples

library(highOrderPortfolios)
data(X50)

X_moments <- estimate_sample_moments(X50[, 1:10])

highOrderPortfolios documentation built on Oct. 20, 2022, 5:06 p.m.