| fGarch-package {fGarch} | R Documentation |
Package of econometric functions for modelling GARCH processes.
| Package: | fGarch |
| Type: | Package |
| Version: | 270.73 |
| Date: | 2008 |
| License: | GPL (>= 2) |
| Copyright: | (c) 1999-2008 Diethelm Wuertz and Rmetrics Foundation |
| URL: | http://www.rmetrics.org |
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 GARCH-type
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 log-likelihood approach under different
assumptions, Normal, Student-t, 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
n-step 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 TS-GARCH model of
Taylor [1986] and Schwert [1989], the GJR-GARCH model of Glosten,
Jaganathan, and Runkle [1993], the T-ARCH model of Zakoian [1993], the
N-ARCH model of Higgins and Bera [1992], and the Log-ARCH 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.
contains functions to simulate artificial GARCH and APARCH time series processes.
Functions:
garchSpec | Specifies an univariate GARCH time series model, |
garchSim | Simulates a GARCH/APARCH process. |
contains functions to fit the parameters of GARCH and APARCH time series processes.
Functions:
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. |
contains functions to forcecast mean and variance of GARCH and APARCH processes.
Functions:
predict | Forecasts from an object of class 'fGARCH'. |
contains functions to model standardized distribution functions.
Functions:
[dpqr]norm | Normal distribution function, |
[dpqr]snorm | Skew Normal distribution function, |
[s]normFit | Fits parameters of [skew] Normal distribution, |
[dpqr]ged | Generalized Error distribution function, |
[dpqr]sged | Skew Generalized Error distribution function, |
[s]gedFit | Fits parameters of [skew] Generalized Error distribution, |
[dpqr]std | standardized Student-t distribution function, |
[dpqr]sstd | Skew standardized Student-t distribution function, |
[s]stdFit | Fits parameters of [skew] Student-t distribution, |
absMoments | Computes absolute Moments of these distribution. |
NOTE: garchOxFit is no longer part of fGarch package. If you are interested to use, please contact us.
contains a Windows interface to OX.
The function garchOxFit interfaces a subset of the functionality
of the G@ARCH 4.0 Package written in Ox.
G@RCH 4.0 is one of the most sophisticated packages for modelling
univariate GARCH processes including GARCH, EGARCH, GJR, APARCH,
IGARCH, FIGARCH, FIEGARCH, FIAPARCH and HYGARCH models. Parameters
can be estimated by approximate (Quasi-) maximum likelihood methods
under four assumptions: normal, Student-t, GED or skewed Student-t
errors.
Diethelm Wuertz and Rmetrics Core Team.