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 |

`nobs` |
a number of observations simulated |

`d.f` |
degrees of freedom parameter for |

`cut` |
a number of observations to be removed to minimise the initial effects |

A list with components:

`h` |
GARCH(1,1) conditional variances |

`eps` |
a series of error term with the conditional variances "h" |

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.

1 2 3 4 5 6 7 | ```
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)
``` |

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