com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch
Class GarchSim

java.lang.Object
  com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.SimpleTimeSeries
      com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GarchSim

All Implemented Interfaces:

TimeSeries<java.lang.Integer,java.lang.Double>

public class GarchSim
extends SimpleTimeSeries

This class simulates the GARCH models.

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.

See Also:

• Wikipedia: GARCH
• Wikipedia: Autoregressive conditional heteroskedasticity

Nested Class Summary

Nested classes/interfaces inherited from class com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.SimpleTimeSeries

SimpleTimeSeries.Iterator

Nested classes/interfaces inherited from interface com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.TimeSeries

TimeSeries.Entry

Constructor Summary

GarchSim(int n, GarchModel model)
Simulate an GARCH model.

GarchSim(int n, GarchModel model, Innovations innovations)
Simulate an GARCH model.

Method Summary

TimeSeries

sigma2() Get a copy of the conditional variances.

Methods inherited from class com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.SimpleTimeSeries

diff, drop, equals, get, hashCode, iterator, lag, lag, size, toArray, toString

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

GarchSim

public GarchSim(int n,
                GarchModel model,
                Innovations innovations)

Simulate an GARCH model.

The innovation length is at least

n + max(a.length, b.length)

Parameters:

n - the length of the time series to generate

model - a GARCH model specification

innovations - the innovations

GarchSim

public GarchSim(int n,
                GarchModel model)

Simulate an GARCH model. The random error terms are drawn from the standard Normal distribution.

Parameters:

n - the length of the time series to generate

model - a GARCH model specification

Method Detail

sigma2

public TimeSeries sigma2()

Get a copy of the conditional variances.

Returns:

a copy of the conditional variances