Returns an array of cells for the model simulated values.
GARCH_SIM(X, Sigmas, Order, mean, alphas, betas, innovation, Nu, T, seed)
- is the univariate time series data (a one dimensional array of cells (e.g. rows or columns)).
- is the univariate time series data (a one dimensional array of cells (e.g. rows or columns)) of the last q realized volatilities.
- is the time order in the data series (i.e. the first data point's corresponding date (earliest date=1 (default), latest date=0)).
Order Description 1 ascending (the first data point corresponds to the earliest date) (default) 0 descending (the first data point corresponds to the latest date)
- is the GARCH model mean (i.e. mu).
- are the parameters of the ARCH(p) component model (starting with the lowest lag).
- are the parameters of the GARCH(q) component model (starting with the lowest lag).
- is the probability distribution function of the innovations/residuals (1=Gaussian (default), 2=t-Distribution, 3=GED).
value Description 1 Gaussian or Normal Distribution (default) 2 Student's t-Distribution 3 Generalized Error Distribution (GED)
- is the shape parameter (or degrees of freedom) of the innovations/residuals probability distribution function.
- is the simulation path time/horizon (expressed in terms of steps beyond end of the time series). If missing, T is set to One(1).
- is an unsigned integer for setting up the random number generator(s).
- The underlying model is described here.
- The time series is homogeneous or equally spaced.
- The time series may include missing values (e.g. #N/A) at either end.
- The number of parameters in the input argument - alpha - determines the order of the ARCH component model.
- The number of parameters in the input argument - beta - determines the order of the GARCH component model.
- By definition, the GARCH_FORE function returns a constant value equal to the model mean (i.e. $\mu$) for all horizons.
- The function GARCH_SIM was added in version 1.63 SHAMROCK.
- Hamilton, J .D.; Time Series Analysis , Princeton University Press (1994), ISBN 0-691-04289-6
- Tsay, Ruey S.; Analysis of Financial Time Series John Wiley & SONS. (2005), ISBN 0-471-690740
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