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fGarch (version 3042.83)

Rmetrics - Autoregressive Conditional Heteroskedastic Modelling

Description

Provides a collection of functions to analyze and model heteroskedastic behavior in financial time series models.

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Version

Install

install.packages('fGarch')

Monthly Downloads

12,892

Version

3042.83

License

GPL (>= 2)

Maintainer

Tobias Setz

Last Published

November 16th, 2017

Functions in fGarch (3042.83)

fGARCHSPEC-class

Class "fGARCHSPEC"
TimeSeriesData

Time Series Data Sets
sgedFit

Skew Generalized Error Distribution Parameter Estimation
sged

Skew Generalized Error Distribution
fGARCH-class

Class "fGARCH"
fGarch-package

Modelling Heterskedasticity in Financial Time Series
std

Student-t Distribution
gedFit

Generalized Error Distribution Parameter Estimation
sstdFit

Skew Student-t Distribution Parameter Estimation
gedSlider

Geeneralized Error Distribution Slider
absMoments

Absolute Moments of GARCH Distributions
garchSim

Univariate GARCH/APARCH Time Series Simulation
ged

Generalized Error Distribution
garchSpec

Univariate GARCH Time Series Specification
sstd

Skew Student-t Distribution and Parameter Estimation
sgedSlider

Skew GED Distribution Slider
snormSlider

Skew Normal Distribution Slider
coef-methods

GARCH Coefficients Methods
snormFit

Skew Normal Distribution Parameter Estimation
fitted-methods

Extract GARCH Model Fitted Values
sstdSlider

Skew Student-t Distribution Slider
predict-methods

GARCH Prediction Function
snorm

Skew Normal Distribution
residuals-methods

Extract GARCH Model Residuals
stdFit

Student-t Distribution Parameter Estimation
volatility-methods

Extract GARCH Model Volatility
stdSlider

Student-t Distribution Slider
formula-methods

Extract GARCH Model formula
plot-methods

GARCH Plot Methods
show-methods

GARCH Modelling Show Methods
garchFit

Univariate GARCH Time Series Fitting
garchFitControl

GARCH Fitting Algorithms and Control
summary-methods

GARCH Summary Methods