Provides the MGARCH-BEKK estimation procedure.
BEKK( eps, order = c(1, 1), params = NULL, fixed = NULL, method = "BFGS", verbose = F )
Data frame holding time series.
BEKK(p, q) order. An integer vector of length 2
giving the orders of the model to be fitted.
Initial parameters for the
Vector of parameters to be fixed.
The method that will be used by the
Indicates if we need verbose output during the estimation.
BEKK estimates a
BEKK(p,q) model, where
stands for the GARCH order, and
q stands for the ARCH
Estimation results packaged as
a data frame contaning all time series
length of the series
order of the BEKK model fitted
time to complete the estimation process
time to complete the whole routine within the mvBEKK.est process
estimation object returned from the optimization process, using
the AIC value of the fitted model
list of estimated parameter matrices
list of asymptotic theory estimates of standard errors of estimated parameters
list of estimated conditional correlation series
list of estimated conditional standard deviation series
list of estimated series of covariance matrices
estimated eigenvalues for sum of Kronecker products
estimated unconditional covariance matrix
list of estimated series of residuals
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., K.F. Kroner, Multivariate simultaneous generalized ARCH, Econometric Theory, 122-150, 1995
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
## Simulate series: simulated <- simulateBEKK(2, 1000, c(1,1)) ## Prepare the matrix: simulated <- do.call(cbind, simulated$eps) ## Estimate with default arguments: estimated <- BEKK(simulated) ## Not run: ## Show diagnostics: diagnoseBEKK(estimated) ## End(Not run)
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