# residualsdcc: Residuals for MGARCH(1,1) DCC models In TaoussD/EbEEMGARCH: Estimating MGARCH(1,1) model equation by equation

## Description

Compute the residuals from the estimation of a MGARCH(1,1) DCC on data

## Usage

 `1` ```residuals_DCC(Omega,A,B,alpha,beta,S,eps,r=10,type) ```

## Arguments

With usual notations

 `Omega` Estimation of Omega `A` Estimation of A `B` Estimation of B `alpha` Estimation of alpha `beta` Estimation of beta `S` Estimation of S `eps` Data used `r` Number of observations for the initial conditions `type` type="Engle" for Engle-DCC type="Aielli" for Aielli-DCC

## Details

Residuals are necessary to compute quantile for the estimation of the VaR of financial series

## Value

With usual notations

 `Ht` List of Ht `Rt` List or Rt `eta` Residuals

## Author(s)

D. Taouss & C. Francq

## References

C. Francq & J.M. Zakoian, Estimating multivariate GARCH and Stochastic Correlation models equation by equation, October 2014
G.P. Aielli, Dynamic Conditional Correlation: on Properties and Estimation, July 2011

`EbEEMGARCH` Homepage of the documentation
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ```#Simulation of the yield of 2 assets m<-2 n <- 800 Omega <- c(0.001, 0.001); A <- matrix(c(0.03, 0.01, 0.01, 0.03), nrow = 2) B <- c(0.1, 0.1); S <- matrix(c(1, 0.4, 0.4, 1), nrow = 2) alpha <- 0.05; beta <- 0.97 - alpha nu <- 14 yield <- GarchDCC.sim(n, Omega, A, B, alpha, beta, S, nu = nu, noise = "student", model = "Aielli") #Estimation of the parameters (3-steps method) EbEE<-estimDCC.EbEE(Omega,A,B,S,alpha,beta,yield\$sim,type="Aielli") var <- residuals_DCC(EbEE\$Omega, EbEE\$A, EbEE\$B, EbEE\$alpha, EbEE\$beta, EbEE\$S, yield\$sim, type="Aielli") ```