GarchModelling {fSeries} | R Documentation |
A collection and description of functions to
simulate artificial ARCH time series processes,
to fit the parameters of univariate time series
to ARCH models, to perform a diagnostic analysis of
the fit, and to predict future values of the time
series.
The family of GARCH time series models includes the following processes:
garch | generalized AR conditional heteroskedastic models, |
aparch | asymmetretic power ARCH models. |
Note: This collection is still under a complete reconstruction.
garchSpec(model = list(omega = 1.0e-6, alpha = 0.1, beta = 0.8), presample = NULL, cond.dist = c("rnorm", "rged", "rstd", "rsnorm", "rsged", "rsstd")) ## S3 method for class 'garchSpec': print(x, ...) garchSim(model = list(omega = 1.0e-6, alpha = 0.1, beta = 0.8), n = 100, presample = NULL, cond.dist = c("rnorm", "rged", "rstd", "rsnorm", "rsged", "rsstd")) garchFit(formula.mean = ~arma(0, 0), formula.var = ~garch(1, 1), series = x, presample = NULL, cond.dist = c("dnorm", "dged", "dstd", "dsnorm", "dsged", "dsstd"), symmetric = TRUE, trace = TRUE, title = NULL, description = NULL, ...) ## S3 method for class 'fGARCH': print(x, ...) ## S3 method for class 'fGARCH': plot(x, ...) ## S3 method for class 'fGARCH': summary(object, ...)
cond.dist |
[garchSpec, garchSim, garchFit] - a character string naming the desired conditional distribution. Valid values are "dnorm" , "dged" , "dstd" ,
"dsnorm" , "dsged" , "dsstd" . The default value
is the normal distribution.
|
description |
[garchFit] - a character string which allows for a brief description. |
formula.mean, formula.var |
[garchFit] - two formula objects describing the mean and variance equation of the ARMA-GARCH/APARCH model. By default a pure GARCH(1,1) mode is selected, this means: formula.mean=~arma(0,0) ,
and formula.var=~garch(1,1) . To specify for example an
APARCH(1,1) use: formula.var=~apaarch(1,1)
|
model |
[garchSpec, garchSim] - List of GARCH model parameters: omega - the constant coefficient of the variance equation; alpha - the vector of autoregressive coefficients; beta - the vector of variance coefficients; Further Optional Values: mu - the mean value; ar - the autoregressive ARMA coefficients; ma - the moving average ARMA coefficients; The default model is Bollerslev's GARCH(1,1) model. |
n |
[garchSim] - length of output series, an integer value. An integer value, by default n=100 .
|
object |
[summary] - an object of class fGARCH as returned from the function
garchFit() .
|
presample |
a numeric three column matrix with start values for the series, innovations, and conditional variances. For an ARMA(m,n)-GARCH(p,q) process the number of rows must be at least max(m,n,p,q), longer presamples are cutted. |
series |
[garchFit] - a numeric vector or univariate timeSeries object to be
fitted. By default series=x .
|
symmetric |
a logical flag for APARCH models. Should the model be leveraged?
By default symmetric=TRUE .
|
title |
[garchFit] - a character string which allows for a project title. |
trace |
[garchFit] - a logical flag. Should the optimization process of fitting the model parameters be printed. By default trace=TRUE .
|
x |
[print][plot] - either an object of class garchSpec for printing specification
structures, or an object of class fGARCH for printing fitted
GARCH/APARCH models or plotting results from the diagnostic analysis
of fitted models.
|
... |
additional arguments to be passed. |
Parameter Estimation:
garchFit
uses the nlminb()
optimizer to find the maximum
likelihood estimates.
garchSpec
returns a S4 object of class fGARCH
with the following slots:
@call |
the call of the garch function.
|
@formula |
a list with two formula entries for the mean and variance equation. |
@model |
a list with the model parameters. |
@presample |
a numeric matrix with presample values. |
@distribution |
a character string with the name of the conditional distribution. |
@call |
the call of the garch function.
|
@formula |
a list with two formula entries for the mean and variance equation. |
@method |
a string denoting the optimization method. |
@fit |
a list with the results from the parameter estimation. |
@residuals |
a numeric vector with the residual values. |
@fitted.values |
a numeric vector with the fitted values. |
@title |
a title string. |
@description |
a string with a brief description. |
Diethelm Wuertz for the Rmetrics R-port.
ATT (1984); PORT Library Documentation, http://netlib.bell-labs.com/netlib/port/.
Bera A.K., Higgins M.L. (1993); ARCH Models: Properties, Estimation and Testing, J. Economic Surveys 7, 305–362.
Bollerslev T. (1986); Generalized Autoregressive Conditional Heteroscedasticity, Journal of Econometrics 31, 307–327.
Engle R.F. (1982); Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation, Econometrica 50, 987–1008.
## SOURCE("fSeries1.34C-GarchModelling") ## Not run: # garchSpec - # garchSim - # garchFit - # For examples we refer to: demo/xmpDWChapter34.R ... ## End(Not run)