fGarchpackage  R Documentation 
The Rmetrics fGarch package is a collection of functions to analyze and model heteroskedastic behavior in financial time series.
GARCH, Generalized Autoregressive Conditional Heteroskedastic, models have become important in the analysis of time series data, particularly in financial applications when the goal is to analyze and forecast volatility.
For this purpose, the family of GARCH functions offers functions for
simulating, estimating and forecasting various univariate GARCHtype
time series models in the conditional variance and an ARMA
specification in the conditional mean. The function
garchFit
is a numerical implementation of the maximum
loglikelihood approach under different assumptions, Normal,
Studentt, GED errors or their skewed versions. The parameter
estimates are checked by several diagnostic analysis tools including
graphical features and hypothesis tests. Functions to compute nstep
ahead forecasts of both the conditional mean and variance are also
available.
The number of GARCH models is immense, but the most influential models
were the first. Beside the standard ARCH model introduced by Engle [1982]
and the GARCH model introduced by Bollerslev [1986], the function
garchFit
also includes the more general class of asymmetric power
ARCH models, named APARCH, introduced by Ding, Granger and Engle [1993].
The APARCH models include as special cases the TSGARCH model of
Taylor [1986] and Schwert [1989], the GJRGARCH model of Glosten,
Jaganathan, and Runkle [1993], the TARCH model of Zakoian [1993], the
NARCH model of Higgins and Bera [1992], and the LogARCH model of
Geweke [1986] and Pentula [1986].
There exist a collection of review articles by Bollerslev, Chou and Kroner [1992], Bera and Higgins [1993], Bollerslev, Engle and Nelson [1994], Engle [2001], Engle and Patton [2001], and Li, Ling and McAleer [2002] which give a good overview of the scope of the research.
Functions to simulate artificial GARCH and APARCH time series processes.
garchSpec  specifies an univariate GARCH time series model 
garchSim  simulates a GARCH/APARCH process 
Functions to fit the parameters of GARCH and APARCH time series processes.
garchFit  fits the parameters of a GARCH process 
residuals  extracts residuals from a fitted "fGARCH" object 
fitted  extracts fitted values from a fitted "fGARCH" object 
volatility  extracts conditional volatility from a fitted "fGARCH" object 
coef  extracts coefficients from a fitted "fGARCH" object 
formula  extracts formula expression from a fitted "fGARCH" object

Functions to forcecast mean and variance of GARCH and APARCH processes.
predict  forecasts from an object of class "fGARCH"

This section contains functions to model standardized distributions.
[dpqr]norm  Normal distribution (base R) 
[dpqr]snorm  Skew normal distribution 
snormFit  fits parameters of Skew normal distribution 
[dpqr]ged  Generalized error distribution 
[dpqr]sged  Skew Generalized error distribution 
gedFit  fits parameters of Generalized error distribution 
sgedFit  fits parameters of Skew generalized error distribution 
[dpqr]std  Standardized Studentt distribution 
[dpqr]sstd  Skew standardized Studentt distribution 
stdFit  fits parameters of Standardized Studentt distribution 
sstdFit  fits parameters of Skew standardized Studentt distribution 
absMoments  computes absolute moments of these distribution 
The fGarch
Rmetrics package is written for educational
support in teaching "Computational Finance and Financial Engineering"
and licensed under the GPL.
Diethelm Wuertz [aut] (original code), Yohan Chalabi [aut], Tobias Setz [aut], Martin Maechler [ctb] (<https://orcid.org/0000000286859910>), Chris Boudt [ctb] Pierre Chausse [ctb], Michal Miklovac [ctb], Georgi N. Boshnakov [cre, ctb]
Maintainer: Georgi N. Boshnakov <georgi.boshnakov@manchester.ac.uk>
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.