gen.garch.wge

Generates a realization of length n from the GARCH(p0,q0) model (4.26) in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

 GARCH 

 Conditional variance

Accompanies the texts Time Series for Data Science with R by Woodward, Sadler and Robertson & Applied Time Series Analysis with R, 2nd edition by Woodward, Gray, and Elliott.  It is helpful for data analysis and for time series instruction.

Bivin Sadler

tswge

Time Series for Data Science

gen.garch.wge function

<dl> <dt>n</dt>
<dd>Length of realization to be generated</dd> <dt>alpha0</dt>
<dd>The constant alpha0 in model (4.23)</dd> <dt>alpha</dt>
<dd>A vector of length q0 containing alpha1 through alphaq0</dd> <dt>beta</dt>
<dd>A vector of length p0 containing beta1 through betap0</dd> <dt>plot</dt>
<dd>If plot=TRUE (default) the generated realization is plotted</dd> <dt>sn</dt>
<dd>determines the seed used in the simulation. sn=0 produces new/random realization each time. sn=positive integer produces same realization each time</dd></dl>

Arguments

Author

Generate a realization from a GARCH(p0,q0) model — gen.garch.wge

<dl>

 <dt>n</dt>
<dd>Length of realization to be generated</dd>

 <dt>alpha0</dt>
<dd>The constant alpha0 in model (4.23)</dd>

 <dt>alpha</dt>
<dd>A vector of length q0 containing alpha1 through alphaq0</dd>

 <dt>beta</dt>
<dd>A vector of length p0 containing beta1 through betap0</dd>

 <dt>plot</dt>
<dd>If plot=TRUE (default) the generated realization is plotted</dd>

 <dt>sn</dt>
<dd>determines the seed used in the simulation. sn=0 produces new/random realization each time. sn=positive integer produces same realization each time</dd>

</dl>

gen.garch.wge: Generate a realization from a GARCH(p0,q0) model

Description

Usage

Value

Arguments

Author

References

Examples