mGJR(p, q, g) order a three element integer vector
giving the order of the model to be fitted. order[2]
refers to the ARCH order and order[1] to the GARCH
order and order[3] to the GJR order.
params
Initial parameters for the optim function.
fixed
A two dimensional vector that contains the user
specified fixed parameter values.
method
The method that will be used by the optim
function. See ?optim for available options.
Value
Estimation results packaged as mGJR class instance. The values are defined as:
eps1
first time series
eps2
second time series
length
length of each series
order
order of the mGJR model fitted
estimation.time
time to complete the estimation process
total.time
time to complete the whole routine within the mGJR.est process
estimation
estimation object returned from the optimization process, using optim
aic
the AIC value of the fitted model
est.params
estimated parameter matrices
asy.se.coef
asymptotic theory estimates of standard errors of estimated parameters
cor
estimated conditional correlation series
sd1
first estimated conditional standard deviation series
sd2
second estimated conditional standard deviation series
H.estimated
estimated series of covariance matrices
eigenvalues
estimated eigenvalues for sum of Kronecker products
uncond.cov.matrix
estimated unconditional covariance matrix
resid1
first estimated series of residuals
resid2
second estimated series of residuals
References
Bauwens L., S. Laurent, J.V.K. Rombouts, Multivariate GARCH models: A survey, April, 2003
Bollerslev T., Modelling the coherence in short-run nominal exchange rate: A multivariate generalized ARCH approach, Review of Economics and Statistics, 498–505, 72, 1990
Engle R.F., Dynamic conditional correlation: A new simple class of multivariate GARCH models, Journal of Business and Economic Statistics, 339–350, 20, 2002
Tse Y.K., A.K.C. Tsui, A multivariate generalized autoregressive conditional heteroscedasticity model with time-varying correlations, Journal of Business and Economic Statistics, 351-362, 20, 2002
Examples
## Not run:
sim = BEKK.sim(1000)
est = mGJR(sim$eps1, sim$eps2)
## End(Not run)