rmnts: rmnts

In aaron9011/temStaR-v0.814: Tempered Stable Distribution

Description

rmnts generates random vector following the n dimensional NTS distribution using Cholesky decomposition.

r = μ + diag(σ) X,

where

X follows stdNTS_n(α, θ, β, Σ)

Usage

rmnts(strMnts, numofsample)

Arguments

numofsample

number of samples.

strPMNTS

Structure of parameters for the n-dimensional NTS distribution. strPMNTS$ndim : dimension strPMNTS$mu : μ mean vector (column vector) of the input data. strPMNTS$sigma : σ standard deviation vector (column vector) of the input data. strPMNTS$alpha : α of the std NTS distribution (X). strPMNTS$theta : θ of the std NTS distribution (X). strPMNTS$beta : β vector (column vector) of the std NTS distribution (X). strPMNTS$Rho : ρ matrix of the std NTS distribution (X).

Value

Simulated NTS random vectors

References

Kim, Y. S. (2020) Portfolio Optimization on the Dispersion Risk and the Asymmetric Tail Risk https://arxiv.org/pdf/2007.13972.pdf

Examples

strPMNTS <- list(ndim = 2,
                 mu = c( 0.00011, 0.00048 ),
                 sigma = c( 0.0162, 0.0231 ),
                 alpha = 1.23,
                 theta = 3.607,
                 beta =  c( -0.1209,  0.0905 ),
                 Rho = matrix( data = c(1.0, 0.55, 0.55, 1.0), nrow = 2, ncol = 2)
)
gensim <- rmnts( strPMNTS, 100 )
plot(gensim)

aaron9011/temStaR-v0.814 documentation built on Dec. 24, 2021, 6:16 p.m.