Description Usage Arguments Details Value Author(s) Examples
Method for creating a univariate GARCH specification object prior to fitting.
1 2 3 4 5  ugarchspec(variance.model = list(model = "sGARCH", garchOrder = c(1, 1),
submodel = NULL, external.regressors = NULL, variance.targeting = FALSE),
mean.model = list(armaOrder = c(1, 1), include.mean = TRUE, archm = FALSE,
archpow = 1, arfima = FALSE, external.regressors = NULL, archex = FALSE),
distribution.model = "norm", start.pars = list(), fixed.pars = list(), ...)

variance.model 
List containing the variance model specification: 
mean.model 
List containing the mean model specification: 
distribution.model 
The conditional density to use for the innovations. Valid choices are “norm” for the normal distibution, “snorm” for the skewnormal distribution, “std” for the studentt, “sstd” for the skewstudent, “ged” for the generalized error distribution, “sged” for the skewgeneralized error distribution, “nig” for the normal inverse gaussian distribution, “ghyp” for the Generalized Hyperbolic, and “jsu” for Johnson's SU distribution. Note that some of the distributions are taken from the fBasics package and implenented locally here for convenience. The “jsu” distribution is the reparametrized version from the “gamlss” package. 
start.pars 
List of staring parameters for the optimization routine. These are not usually required unless the optimization has problems converging. 
fixed.pars 
List of parameters which are to be kept fixed during the optimization. It is
possible that you designate all parameters as fixed so as to quickly recover
just the results of some previous work or published work. The optional argument
“fixed.se” in the 
... 
. 
The specification allows for a wide choice in univariate GARCH models,
distributions, and mean equation modelling. For the “fGARCH” model,
this represents Hentschel's omnibus model which subsumes many others.
For the mean equation, ARFIMAX is fully supported in fitting, forecasting and
simulation. There is also an option to multiply the external regressors by
the conditional standard deviation, which may be of use for example in
calculating the correlation coefficient in a CAPM type setting.
The “iGARCH” implements the integrated GARCH model. For the “EWMA”
model just set “omega” to zero in the fixed parameters list.
The asymmetry term in the rugarch package, for all implemented models, follows
the order of the arch parameter alpha
.
Variance targeting, referred to in Engle and Mezrich (1996), replaces the
intercept “omega” in the variance equation by 1 minus the persistence
multiplied by the unconditional variance which is calculated by its sample
counterpart in the squared residuals during estimation. In the presence of
external regressors in the variance equation, the sample average of the external
regresssors is multiplied by their coefficient and subtracted from the
variance target.
In order to understand which parameters can be entered in the start.pars and
fixed.pars optional arguments, the list below exposes the names used for the
parameters across the various models:(note that when a parameter is followed by
a number, this represents the order of the model. Just increment the number
for higher orders, with the exception of the component sGARCH permanent
component parameters which are fixed to have a lag1 autoregressive structure.):
Mean Model
constant: mu
AR term: ar1
MA term: ma1
ARCHinmean: archm
exogenous regressors: mxreg1
arfima: arfima
Distribution Model
skew: skew
shape: shape
ghlambda: lambda (for GHYP distribution)
Variance Model (common specs)
constant: omega
ARCH term: alpha1
GARCH term: beta1
exogenous regressors: vxreg1
Variance Model (GJR, EGARCH)
assymetry term: gamma1
Variance Model (APARCH)
assymetry term: gamma1
power term: delta
Variance Model (FGARCH)
assymetry term1 (rotation): eta11
assymetry term2 (shift): eta21
power term1(shock): delta
power term2(variance): lambda
Variance Model (csGARCH)
permanent component autoregressive term (rho): eta11
permanent component shock term (phi): eta21
permanent component intercept: omega
transitory component ARCH term: alpha1
transitory component GARCH term: beta1
The terms defined above are better explained in the vignette which provides each model's specification and exact representation. For instance, in the eGARCH model, both alpha and gamma jointly determine the assymetry, and relate to the magnitude and sign of the standardized innovations.
A uGARCHspec
object containing details of the GARCH
specification.
Alexios Ghalanos
1 2 3 4 5 6 7 8 9 10 11  # a standard specification
spec1 = ugarchspec()
spec1
# an example which keep the ar1 and ma1 coefficients fixed:
spec2 = ugarchspec(mean.model=list(armaOrder=c(2,2),
fixed.pars=list(ar1=0.3,ma1=0.3)))
spec2
# an example of the EWMA Model
spec3 = ugarchspec(variance.model=list(model="iGARCH", garchOrder=c(1,1)),
mean.model=list(armaOrder=c(0,0), include.mean=TRUE),
distribution.model="norm", fixed.pars=list(omega=0))

Loading required package: parallel
Attaching package: 'rugarch'
The following object is masked from 'package:stats':
sigma
Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl_init' failed, running with rgl.useNULL = TRUE
3: .onUnload failed in unloadNamespace() for 'rgl', details:
call: fun(...)
error: object 'rgl_quit' not found
**
* GARCH Model Spec *
**
Conditional Variance Dynamics

GARCH Model : sGARCH(1,1)
Variance Targeting : FALSE
Conditional Mean Dynamics

Mean Model : ARFIMA(1,0,1)
Include Mean : TRUE
GARCHinMean : FALSE
Conditional Distribution

Distribution : norm
Includes Skew : FALSE
Includes Shape : FALSE
Includes Lambda : FALSE
Warning message:
unidentified option(s) in mean.model:
fixed.pars
**
* GARCH Model Spec *
**
Conditional Variance Dynamics

GARCH Model : sGARCH(1,1)
Variance Targeting : FALSE
Conditional Mean Dynamics

Mean Model : ARFIMA(2,0,2)
Include Mean : TRUE
GARCHinMean : FALSE
Conditional Distribution

Distribution : norm
Includes Skew : FALSE
Includes Shape : FALSE
Includes Lambda : FALSE
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