garchdccsim: Simulation of a MGARCH(1,1) DCC semi-diagonal

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

Description

Simulation of a Engle or Aielli MGARCH(1,1) DCC semi-diagonal with student or normal noise

Usage

GarchDCC.sim(n, Omega, A, B, alpha, beta, S, nu = Inf, valinit = 500, model, noise)

Arguments

With usual notations of MGARCH(1,1) DCCC

n	Number of observation
Omega	Vector Omega (constant)
A	Matrix A
B	Vector of the diagonal of B
alpha	Scalar alpha in Aielli's notation
beta	Scalar beta in Aielli's notation
S	Variance of the noise (matrix)
nu	Degrees of freedom of the t-distribution, leave blank if normal-noise
valinit	Burn-in
model	type="Engle" for estimation as an Engle-DCC type="Aielli" for estimation as an Aielli-DCC
noise	"normal" or "student"

Value

eps	Simulations
cor	Correlation Matrix

See Also

EbEEMGARCH Homepage of the documentation

Examples

###
#Simulation of a Aielli DCC semi-diagonal with student noise
###

Omega <- c(0.01, 0.01);
A <- matrix(c(0.03, 0.01, 0.01, 0.03), nrow = 2)
B <- c(0.8, 0.8);
S <- matrix(c(1, 0.4, 0.4, 1), nrow = 2)
alpha <- 0.05;
beta <- 0.99 - alpha
n <- 2500
nu <-14

eps <- GarchDCC.sim(n, Omega, A, B, alpha, beta, S, nu = nu, noise = "student",model="Aielli")

###
#Simulation of a Engle DCC semi-diagonal with normal noise
###

eps <- GarchDCC.sim(n, Omega, A, B, alpha, beta, S, nu = Inf, noise = "normal",model="Engle")

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