logLikDccGarch {bayesDccGarch} | R Documentation |
Compute the logarithm of likelihood function of DCC-GARCH(1,1) Model if mY
is a matrix or the logarithm of likelihood function of GARCH(1,1) Model if mY
is numeric vector.
logLikDccGarch(mY, omega = rep(0.03, ncol(mY)), alpha = rep(0.03, ncol(mY)), beta = rep(0.8, ncol(mY)), a = 0.03, b = 0.8, gamma = rep(1, ncol(mY)), tail = 10, errorDist = 2)
mY |
a matrix of the data (n \times k). |
omega |
a numeric vector (k \times 1) with the the values of ω_i parameters. Default: |
alpha |
a numeric vector (k \times 1) with the the values of α_i parameters. Default: |
beta |
a numeric vector (k \times 1) with the the values of β_i parameters. Default: |
a |
a numeric value of the a parameter. Default: |
b |
a numeric value of the b parameter. Default: |
gamma |
a numeric vector (k \times 1) with the values of γ_i parameters. Default: |
tail |
a numeric value of ν parameter if |
errorDist |
a probability distribution for errors. Use |
The log-likelihood of the model GARCH(1,1) is computed if mY
has just one column. The arguments a
and b
are not consider in this case.
Return a list with the elements:
$H |
a matrix where the lines are the H_t values for t=1,...,n. |
$value |
the value of the logarithm of likelihood function. |
Jose Augusto Fioruci, Ricardo Sandes Ehlers and Francisco Louzada
Fioruci, J.A., Ehlers, R.S., Andrade Filho, M.G. Bayesian multivariate GARCH models with dynamic correlations and asymmetric error distributions, Journal of Applied Statistics, 41(2), 320–331, 2014a. http://dx.doi.org/10.1080/02664763.2013.839635.
Fioruci, J.A., Ehlers, R.S., Louzada, F. BayesDccGarch - An Implementation of Multivariate GARCH DCC Models, ArXiv e-prints, 2014b. http://adsabs.harvard.edu/abs/2014arXiv1412.2967F.
bayesDccGarch-package
, bayesDccGarch
data(DaxCacNik) Dax = DaxCacNik[,1] ###### log-likelihood function of GARCH(1,1) model with SST innovations #### logLikDccGarch(Dax, omega=0.03, alpha=0.03, beta=0.8, gamma=0.7)$value ###### log-likelihood function of DCC-GARCH(1,1) model with SST innovations #### logLikDccGarch(DaxCacNik, beta=c(0.82,0.91,0.85), gamma=c(0.7, 1.3, 1.7), tail=10)$value