The Rmetrics "fGarch" package is a collection of functions to analyze and model heteroskedastic behavior in financial time series models. .
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.
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.
garchFit fits the parameters of a GARCH process
Extractor Functions:
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.
predict forecasts from an object of class 'fGARCH'
This section contains functions to model standardized distribution functions.
Skew Normal Distribution:
[dpqr]norm Normal distribution function
[dpqr]snorm Skew Normal distribution function
[s]normFit fits parameters of [skew] Normal distribution
Skew Generalized Error Distribution:
[dpqr]ged Generalized Error distribution function
[dpqr]sged Skew Generalized Error distribution function
[s]gedFit fits parameters of [skew] Generalized Error distribution
Skew Standardized Student-t 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
Abdolute Moments:
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.
| Package: | fGarch |
| Type: | Package |
| Version: | R 3.0.1 |
| Date: | 2014 |
| License: | GPL Version 2 or later |
| Copyright: | (c) 1999-2014 Rmetrics Assiciation |
| URL: | https://www.rmetrics.org |