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Last data update: 2014.03.03

R: Estimate MGARCH-BEKK processes

BEKK	R Documentation

Estimate MGARCH-BEKK processes

Description

Provides the MGARCH-BEKK estimation procedure.

Usage

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

Arguments

`eps`	Data frame holding time series.
`order`	BEKK(p, q) 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`	Initial parameters for the `optim` function.
`fixed`	Vector of parameters to be fixed.
`method`	The method that will be used by the `optim` function.
`verbose`	Indicates if we need verbose output during the estimation.

Details

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

Value

Estimation results packaged as BEKK class instance.

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

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., 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

Examples

## 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)