eval_portfolio_moments: Evaluate first four moments of a given portfolio

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

Evaluate first four moments of a given portfolio

Description

Evaluate first four moments of a given portfolio's return, namely, mean, variance, skewness, and kurtosis.

Usage

eval_portfolio_moments(w, X_statistics)

Arguments

w	Numerical vector with portfolio weights.
X_statistics	Argument characterizing the constituents assets. Either the sample parameters as obtained by function estimate_sample_moments() or the multivariate skew t parameters as obtained by function estimate_skew_t().

Value

Four moments of the given portfolio.

Author(s)

Rui Zhou, Xiwen Wang, 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>.

X. Wang, R. Zhou, J. Ying, and D. P. Palomar, "Efficient and Scalable High-Order Portfolios Design via Parametric Skew-t Distribution," Available in arXiv, 2022. <https://arxiv.org/pdf/2206.02412v1.pdf>.

Examples

library(highOrderPortfolios)
data(X50)

# nonparametric case
X_moments <- estimate_sample_moments(X50[, 1:10])
w_moments <- eval_portfolio_moments(w = rep(1/10, 10), X_statistics = X_moments)

# parametric case (based on the multivariate skew t distribution)
X_skew_t_params <- estimate_skew_t(X50[, 1:10])
w_moments <- eval_portfolio_moments(w = rep(1/10, 10), X_statistics = X_skew_t_params)

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