# 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

 `1` ```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

`EbEEMGARCH` Homepage of the documentation
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20``` ```### #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") ```