mvBEKK.est: Estimate a BEKK(p,q) model
In vst/mgarch: Tools for simulating, estimating and diagnosing MGARCH processes

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

mvBEKK.est estimates a BEKK(p,q) model for two given time series

1	mvBEKK.est(eps, order = c(1,1), params = NULL, fixed = NULL, method = "BFGS", verbose = F)

`eps`	a data frame contaning all the time series
`order`	an integer vector of length 2 giving the orders of the model to be fitted. `order[2]` refers to the ARCH order and `order[1]` to the GARCH order.
`params`	a vector of initial parameters for the optimization process
`fixed`	a two dimensional array that contains the user specified fixed parameter values.
`method`	method to be used in the optimization process. See `optim` function for available options.
`verbose`	If set to True, verbose output will be printed on the screen

mvBEKK.est estimates a BEKK(p,q) model, where p stands for the GARCH order, and q stands for the ARCH order

A list of class "mvBEKK.est" with the following elements:

`eps`	a data frame contaning all time series
`length`	length of the series
`order`	order of the BEKK model fitted
`estimation.time`	time to complete the estimation process
`total.time`	time to complete the whole routine within the mvBEKK.est process
`estimation`	estimation object returned from the optimization process, using `optim`
`aic`	the AIC value of the fitted model
`est.params`	list of estimated parameter matrices
`asy.se.coef`	list of asymptotic theory estimates of standard errors of estimated parameters
`cor`	list of estimated conditional correlation series
`sd`	list of estimated conditional standard deviation series
`H.estimated`	list of estimated series of covariance matrices
`eigenvalues`	estimated eigenvalues for sum of Kronecker products
`uncond.cov.matrix`	estimated unconditional covariance matrix
`residuals`	list of estimated series of residuals

Harald SCHMIDBAUER harald@hs-stat.com, Vehbi Sinan TUNALIOGLU vst@vsthost.com

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

mvBEKK.sim for simulation of BEKK models; mvBEKK.diag for printing out mvBEKK.est estimation object in a fancy format.

## Not run: 
sim = mvBEKK.sim(series.count = 3, T = 1000) # simulate a 3 dimensional mgarch model with length of 1000
eps = data.frame(sim$eps[[1]], sim$eps[[2]], sim$eps[[3]]) # encapsulate
est = mvBEKK.est(eps) # estimate the simulated model

## End(Not run)