mgarchsim: Simulation of a MGARCH(1,1) CCC-diagonal or semi-diagonal

In TaoussD/EbEEMGARCH: Estimating MGARCH(1,1) model equation by equation

Description

Fonction to simulate data from MGARCH(1,1) CCC-diagonal or semi-diagonal model

Usage

GarchCCC.sim(n, omega, alpha, beta, model, R, noise, nu = Inf, valinit = 500)

Arguments

n	Number of observation
omega	Vector Omega
alpha	Vector of the diagonal of Alpha
beta	Vector of the diagonal of Beta
model	model="diagonal" if MGARCH(1,1) diagonal model="sdiagonal" if MGARCH(1,1) is semi-diagonal i.e. Alpha is plain and Beta diagonal
R	Variance of the noise (matrix)
noise	"normal" or "student"
nu	Degrees of freedom of the t-distribution, leave blank if normal-noise
valinit	Burn-in

Value

dataframe of the observations

Author(s)

D. Taouss & C. Francq

See Also

EbEEMGARCH Homepage of the documentation

Examples

####
# Sampling a diagonal model with normal noise
####

m <- 3 #Number of assets in the portfolio
Omega0 <- rep(0.01, m)
Alpha0 <- rep(0.05, m)
Beta0 <- rep(0.90, m)
R0 <- diag(rep(1, m))

Epsi <- GarchCCC.sim(2500, Omega0, Alpha0, Beta0,"diagonal", R0, "normal")

####
# Sampling a semi-d1iagonal model with student noise
####

m <- 3 #Number of assets in the portfolio
Omega0 <- rep(0.01, m)
Alpha0 <- matrix(c(1,0.5,0.5,0.5,1,0.5,0.5,0.5,1),nrow=3)
Beta0 <- rep(0.90, m)
R0 <- diag(rep(1, m))

Epsi <- GarchCCC.sim(2500, Omega0, Alpha0, Beta0,"sdiagonal", R0, "student",7)

TaoussD/EbEEMGARCH documentation built on May 9, 2019, 4:18 p.m.