garchm_estim {fEGarch} | R Documentation |
General GARCH-Type Model Estimation
Description
Fit any of the additional short- or long-memory GARCH-type
models from the fEGarch
package aside from those
of the extended EGARCH family.
Usage
garchm_estim(
rt,
model = c("garch", "gjrgarch", "tgarch", "aparch", "figarch", "figjrgarch", "fitgarch",
"fiaparch"),
orders = c(1, 1),
cond_dist = c("norm", "std", "ged", "ald", "snorm", "sstd", "sged", "sald"),
drange = c(0, 1),
meanspec = mean_spec(),
Drange = c(0, 1),
nonparspec = locpol_spec(),
use_nonpar = FALSE,
n_test = 0,
start_pars = NULL,
LB = NULL,
UB = NULL,
control = list(),
control_nonpar = list(),
mean_after_nonpar = FALSE,
parallel = TRUE,
ncores = max(1, future::availableCores() - 1),
trunc = "none",
presample = 50,
Prange = c(1, 5)
)
Arguments
rt |
the observed series ordered from past to present; can be
a numeric vector, a |
model |
any character object among |
orders |
a two-element numeric vector containing the two model
orders |
cond_dist |
the conditional distribution to consider as a
character object; the default is a conditional normal distribution
|
drange |
a two-element numeric vector that gives the boundaries of the
search interval for the fractional differencing parameter |
meanspec |
an object of class "mean_spec"; indicates the specifications for the model in the conditional mean. |
Drange |
a two-element numeric vector that indicates the boundaries
of the interval over which to search for the fractional differencing
parameter |
nonparspec |
an object of class |
use_nonpar |
a logical indicating whether or not to implement a
semiparametric extension of the volatility model defined through |
n_test |
a single numerical value indicating, how many observations
at the end of |
start_pars |
the starting parameters for the numerical optimization
routine; should be of the same length as the parameter output vector
within the output object (also keeping the same order); for |
LB |
the lower boundaries of the parameters in the numerical optimization
routine; should be of the same length as the parameter output vector
within the output object (also keeping the same order); for |
UB |
the upper boundaries of the parameters in the numerical optimization
routine; should be of the same length as the parameter output vector
within the output object (also keeping the same order); for |
control |
a list that is passed to |
control_nonpar |
a list containing changes to the arguments
for the hyperparameter estimation algorithm in the nonparametric
scale function estimation for
|
mean_after_nonpar |
only for |
parallel |
only relevant for a (skewed) average Laplace (AL)
distribution, i.e.
if |
ncores |
only relevant for a (skewed) average Laplace (AL)
distribution, i.e.
if |
trunc |
a positive integer indicating the finite truncation length of the
infinite-order polynomials of the infinite-order representations of the
long-memory model parts; the character |
presample |
the presample length for initialization (for extended EGARCH- / Log-GARCH-type models only relevant for the FARIMA-part, as series in log-transformed conditional variance are initialized by zero). |
Prange |
a two-element vector that indicates the search boundaries for
the parameter |
Details
See the documentation on garch
, gjrgarch
,
tgarch
, aparch
, figarch
,
figjrgarch
, fitgarch
and fiaparch
for more detailed
information on the corresponding models and functions selectable through this
wrapper function.
Value
An object of S4-class "fEGarch_fit_garch"
, "fEGarch_fit_gjrgarch"
,
"fEGarch_fit_tgarch"
,
"fEGarch_fit_aparch"
, "fEGarch_fit_figarch"
, "fEGarch_fit_figjrgarch"
,
"fEGarch_fit_fitgarch"
or "fEGarch_fit_fiaparch"
is returned depending on the selected input for the argument
model
. The object then contains the following elements.
pars
:a named numeric vector with the parameter estimates.
se
:a named numeric vector with the obtained standard errors in accordance with the parameter estimates.
vcov_mat
:the variance-covariance matrix of the parameter estimates with named columns and rows.
rt
:the input object
rt
(or at least the training data, ifn_test
is greater than zero); ifrt
was a"zoo"
or"ts"
object, the formatting is kept.cmeans
:the estimated conditional means; if
rt
was a"zoo"
or"ts"
object, the formatting is also applied tocmeans
.sigt
:the estimated conditional standard deviations (or for
use_nonpar = TRUE
the estimated total volatilities, i.e. scale function value times conditional standard deviation); ifrt
was a"zoo"
or"ts"
object, the formatting is also applied tosigt
.etat
:the obtained residuals; if
rt
was a"zoo"
or"ts"
object, the formatting is also applied toetat
.orders
:a two-element numeric vector stating the considered model orders.
cond_dist
:a character value stating the conditional distribution considered in the model fitting.
long_memo
:a logical value stating whether or not long memory was considered in the model fitting.
llhood
:the log-likelihood value obtained at the optimal parameter combination.
inf_criteria
:a named two-element numeric vector with the corresponding AIC (first element) and BIC (second element) of the fitted parametric model part; for purely parametric models, these criteria are valid for the entire model; for semiparametric models, they are only valid for the parametric step and are not valid for the entire model.
meanspec
:the settings for the model in the conditional mean; is an object of class
"mean_spec"
that is identical to the object passed to the input argumentmeanspec
.test_obs
:the observations at the end up the input
rt
reserved for testing followingn_test
.scale_fun
:the estimated scale function values, if
use_nonpar = TRUE
, otherwiseNULL
; formatting ofrt
is reused.nonpar_model
:the estimation object returned by
tsmooth
ortsmoothlm
foruse_nonpar = TRUE
.trunc
:the input argument
trunc
.
Examples
window.zoo <- get("window.zoo", envir = asNamespace("zoo"))
rt <- window.zoo(SP500, end = "2002-12-31")
model <- garchm_estim(rt, model = "garch")
model