msddcc: Compute mean and variance of estimators through Monte-Carlo...

Description Usage Arguments Value Note Author(s) References See Also Examples

Description

Generate a sample of the estimator to compute mean and standard error

Usage

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MSD.DCC.EbEE(theta0, init, nobs, iter, type, noise, nu=Inf)

Arguments

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
type="Aielli" for estimation as an Aielli-DCC

noise

"normal" or "student"

nu

Degrees of freedom of the t-distribution, leave blank if normal-noise

Value

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

Note

Can take a lot of time if iter is big

Author(s)

D. Taouss & C. Francq

References

C. Francq & J.M. Zakoian, Estimating multivariate GARCH and Stochastic Correlation models equation by equation, October 2014

See Also

EbEEMGARCH Homepage of the documentation

Examples

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#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)

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