public class GARCHSim extends SimpleTimeSeries
An AutoRegressive Moving Average model (ARMA model) is assumed for the error variance, the model is a Generalized AutoRegressive Conditional Heteroskedasticity (GARCH, Bollerslev(1986)) model.
An AutoRegressive Conditional Heteroskedasticity (ARCH) models are used to characterize and model observed time series. They are used whenever there's reason to believe that, at any point in a series, the terms will have a characteristic size, or variance. In particular ARCH models assume the variance of the current error term or innovation to be a function of the actual sizes of the previous time periods' error terms: often the variance is related to the squares of the previous innovations.
TimeSeries.Entry| Constructor and Description |
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GARCHSim(int n,
GARCHModel model)
Simulate an GARCH model.
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GARCHSim(int n,
GARCHModel model,
double[] innovations)
Simulate an GARCH model.
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| Modifier and Type | Method and Description |
|---|---|
TimeSeries |
sigma2()
Get a copy of the conditional variances.
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diff, drop, equals, get, hashCode, iterator, lag, lag, size, toArray, toStringpublic GARCHSim(int n,
GARCHModel model,
double[] innovations)
The innovation length is at least
n + max(a.length, b.length)
n - the length of the time series to generatemodel - a GARCH model specificationinnovations - the innovationspublic GARCHSim(int n,
GARCHModel model)
n - the length of the time series to generatemodel - a GARCH model specificationpublic TimeSeries sigma2()