fgarch_1_1

`fgarch_1_1` Simulates an fGARCH(1,1) process with N independent observations, each observed

Provides a collection of white noise hypothesis tests for functional time series and related visualizations.
These include tests based on the norms of autocovariance operators that are built under both strong and weak
white noise assumptions. Additionally, tests based on the spectral density operator and on principal component
dimensional reduction are included, which are built under strong white noise assumptions.
Also, this package provides goodness-of-fit tests for functional autoregressive of order 1 models.
These methods are described in Kokoszka et al. (2017) <doi:10.1016/j.jmva.2017.08.004>, Characiejus and Rice (2019)
<doi:10.1016/j.ecosta.2019.01.003>, Gabrys and Kokoszka (2007) <doi:10.1198/016214507000001111>,
and Kim et al. (2023) <doi: 10.1214/23-SS143>
respectively.

Mihyun Kim

wwntests

Hypothesis Tests for Functional Time Series

Daniel Petoukhov

fgarch_1_1 function

<dl><dt>N</dt>
<dd>the number of fGARCH(1,1) curves to sample.</dd>
<dt>J</dt>
<dd>the number of points at which each curve is sampled (the resolution of the data).</dd>
<dt>delta</dt>
<dd>a parameter used in the variance recursion of the model.</dd>
<dt>burn_in</dt>
<dd>the number of initial samples to burn (discard).</dd></dl>

Arguments

`fgarch_1_1` Simulates an fGARCH(1,1) process with N independent observations, each observed — fgarch_1_1

<dl>

<dt>N</dt>
<dd>the number of fGARCH(1,1) curves to sample.</dd>


<dt>J</dt>
<dd>the number of points at which each curve is sampled (the resolution of the data).</dd>


<dt>delta</dt>
<dd>a parameter used in the variance recursion of the model.</dd>


<dt>burn_in</dt>
<dd>the number of initial samples to burn (discard).</dd>

</dl>

fgarch_1_1: `fgarch_1_1` Simulates an fGARCH(1,1) process with N independent observations, each observed

Description

Usage

Value

Arguments

Examples