Description Usage Arguments Value Note Author(s) References See Also Examples
Generate a sample of the estimator to compute mean and standard error
1 | MSD.DCC.EbEE(theta0, init, nobs, iter, type, noise, nu=Inf)
|
theta0 |
List of the real parameters |
init |
List of initialisation parameters |
nobs |
Number of observations in the sample |
iter |
Number of iterations |
type |
type="Engel" for estimation as an Engle-DCC
|
noise |
"normal" or "student" |
nu |
Degrees of freedom of the t-distribution, leave blank if normal-noise |
With usual notations of GARCH(1,1) DCC models
Omega.mean |
Mean of Omega |
Omega.sd |
Standard deviation of Omega |
A.mean |
Mean of A |
A.sd |
Standard deviation of A |
B.mean |
Mean of B |
B.sd |
Standard deviation of B |
S.mean |
Mean of S, correlation matrix |
S.sd |
Standard deviation of S |
alpha.mean |
Mean of alpha |
alpha.sd |
Standard deviation of alpha |
beta.mean |
Mean of beta |
beta.sd |
Standard deviation of beta |
Can take a lot of time if iter is big
D. Taouss & C. Francq
C. Francq & J.M. Zakoian, Estimating multivariate GARCH and Stochastic Correlation models equation by equation, October 2014
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 21 22 23 24 | #Sampling some data
m<-2
Omega <- c(1, 1);
A <- matrix(rep(0.025, m ^ 2), ncol = m)
B <- c(0.8, 0.8);
S <- matrix(c(1, 0.3, 0.3, 1), nrow = 2)
alpha <- 0.05;
beta <- 0.99 - alpha
n <- 2500
nu <-7
eps <- GarchDCC.sim(n, Omega, A, B, alpha, beta, S, nu = nu, model="Aielli",noise = "student")
m<-2
omegainit <- rep(0.02, m)
Ainit <- matrix(rep(0.03, m ^ 2), ncol = m)
Binit <- rep(0.7, m)
Sinit<-diag(rep(1,m))
alphainit <- 0.05
betainit <- 0.90 - alphainit
init<-list(A=Ainit,B=Binit,S=Sinit,alpha=alphainit,beta=betainit,Omega=omegainit)
theta0<-list(Omega=Omega,A=A,B=B,alpha=alpha,beta=beta,S=S)
MSD.DCC.EbEE(theta0,init,2000,5,type="Aielli",noise="student",nu=7)
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