simulateBEKK {mgarchBEKK}

Simulate BEKK processes

Description

Provides a procedure to simulate BEKK processes.

Usage

simulateBEKK(series.count, T, order = c(1, 1), params = NULL)

Arguments

series.count

The number of series to be simulated.

T

The length of series to be simulated.

order

BEKK(p, q) order. An integer vector of length 2 giving the orders of the model to fit. order[2] refers to the ARCH order and order[1] to the GARCH order.

params

A vector containing a sequence of parameter matrices' values.

Details

simulateBEKK simulates an N dimensional BEKK(p,q) model for the given length, order list, and initial parameter list where N is also specified by the user.

Value

Simulated series and auxiliary information packaged as a simulateBEKK class instance. Values are:

length

length of the series simulated

order

order of the BEKK model

params

a vector of the selected parameters

true.params

list of parameters in matrix form

eigenvalues

computed eigenvalues for sum of Kronecker products

uncond.cov.matrix

unconditional covariance matrix of the process

white.noise

white noise series used for simulating the process

eps

a list of simulated series

cor

list of series of conditional correlations

sd

list of series of conditional standard deviations

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))