uni_vola_sim: Simulating a series with univariate GARCH(1,1) conditional...
In ccgarch: Conditional Correlation GARCH models

Description Usage Arguments Value Note References See Also Examples

This function simulates an univariate time series with GARCH(1,1) conditional variances.

1	uni.vola.sim(a, nobs, d.f=Inf, cut=1000)

`a`	a vector of parameters (3 \times 1)
`nobs`	a number of observations simulated (T)
`d.f`	degrees of freedom parameter for t-distribution
`cut`	a number of observations to be removed to minimise the initial effects

A list with components:

`h`	GARCH(1,1) conditional variances (T \times 1)
`eps`	a series of error term with the conditional variances "h" (T \times 1)

When d.f=Inf, the innovations (the standardised residuals) follow the standard normal distribution. Otherwise, they follow a student's t-distribution with d.f degrees of freedom.

Bollerslev, T. (1986), “Generalized Autoregressive Conditional Heteroskedasticity”, Journal of Econometrics, 31, 307–327.

Fiorentini, G., G. Calzolari and L. Panattoni (1996), “Analytic Derivatives and the Computation of GARCH Estimates”, Journal of Applied Econometrics, 11, 399–417.

uni.vola

nobs <- 1000
nu <- 8
a <- c(0.1,0.2,0.7)           #  a <- c(a constant, ARCH parameter, GARCH parameter) 
# with normal innovations
eps <- uni.vola.sim(a, nobs)
# with t innovations
eps.t <- uni.vola.sim(a, nobs, d.f = df)