| garchFit {fGarch} | R Documentation |
Estimates the parameters of an univariate ARMA-GARCH/APARCH process.
garchFit(formula = ~ garch(1, 1), data = dem2gbp,
init.rec = c("mci", "uev"),
delta = 2, skew = 1, shape = 4,
cond.dist = c("norm", "snorm", "ged", "sged", "std", "sstd",
"snig", "QMLE"),
include.mean = TRUE, include.delta = NULL, include.skew = NULL,
include.shape = NULL, leverage = NULL, trace = TRUE,
algorithm = c("nlminb", "lbfgsb", "nlminb+nm", "lbfgsb+nm"),
hessian = c("ropt", "rcd"), control = list(),
title = NULL, description = NULL, ...)
garchKappa(cond.dist = c("norm", "ged", "std", "snorm", "sged", "sstd",
"snig"), gamma = 0, delta = 2, skew = NA, shape = NA)
algorithm |
a string parameter that determines the algorithm used for maximum likelihood estimation. |
cond.dist |
a character string naming the desired conditional distribution.
Valid values are "dnorm", "dged", "dstd",
"dsnorm", "dsged", "dsstd" and
"QMLE". The default value is the normal distribution. See
Details for more information.
|
control |
control parameters, the same as used for the functions from
nlminb, and 'bfgs' and 'Nelder-Mead' from optim.
|
data |
an optional timeSeries or data frame object containing the variables
in the model. If not found in data, the variables are taken
from environment(formula), typically the environment from which
armaFit is called. If data is an univariate series, then
the series is converted into a numeric vector and the name of the
response in the formula will be neglected.
|
delta |
a numeric value, the exponent delta of the variance recursion.
By default, this value will be fixed, otherwise the exponent will be
estimated together with the other model parameters if
include.delta=FALSE.
|
description |
a character string which allows for a brief description. |
formula |
formula object describing the mean and variance equation of the
ARMA-GARCH/APARCH model. A pure GARCH(1,1) model is selected
when e.g. formula=~garch(1,1). To specify for example an
ARMA(2,1)-APARCH(1,1) use formula = ~arma(2,1)+apaarch(1,1).
|
gamma |
APARCH leverage parameter entering into the formula for calculating the expectation value. |
hessian |
a string denoting how the Hessian matrix should be evaluated,
either hessian ="rcd", or "ropt", the default,
"rcd" is a central difference approximation implemented
in R and "ropt" use the internal R function optimhess.
|
include.delta |
a logical flag which determines if the parameter for the recursion
equation delta will be estimated or not. If
include.delta=FALSE then the shape parameter will be kept
fixed during the process of parameter optimization.
|
include.mean |
this flag determines if the parameter for the mean will be estimated
or not. If include.mean=TRUE this will be the case, otherwise
the parameter will be kept fixed durcing the process
of parameter optimization.
|
include.shape |
a logical flag which determines if the parameter for the shape
of the conditional distribution will be estimated or not. If
include.shape=FALSE then the shape parameter will be kept
fixed during the process of parameter optimization.
|
include.skew |
a logical flag which determines if the parameter for the skewness
of the conditional distribution will be estimated or not. If
include.skew=FALSE then the skewness parameter will be kept
fixed during the process of parameter optimization.
|
init.rec |
a character string indicating the method how to initialize the mean and varaince recursion relation. |
leverage |
a logical flag for APARCH models. Should the model be leveraged?
By default leverage=TRUE.
|
shape |
a numeric value, the shape parameter of the conditional distribution. |
skew |
a numeric value, the skewness parameter of the conditional distribution. |
title |
a character string which allows for a project title. |
trace |
a logical flag. Should the optimization process of fitting the
model parameters be printed? By default trace=TRUE.
|
... |
additional arguments to be passed. |
"QMLE" stands for Quasi-Maximum Likelihood Estimation, which
assumes normal distribution and uses robust standard errors for
inference. Bollerslev and Wooldridge (1992) proved that if the mean
and the volatility equations are correctly specified, the QML
estimates are consistent and asymptotically normally
distributed. However, the estimates are not efficient and “the
efficiency loss can be marked under asymmetric ... distributions”
(Bollerslev and Wooldridge (1992), p. 166). The robust
variance-covariance matrix of the estimates equals the (Eicker-White)
sandwich estimator, i.e.
V = H^(-1) G' G H^(-1),
where V denotes the variance-covariance matrix, H stands for the Hessian and G represents the matrix of contributions to the gradient, the elements of which are defined as
G_{t,i} = derivative of l_{t} w.r.t. zeta_{i},
where l_{t} is the log likelihood of the t-th observation and zeta_{i} is the i-th estimated parameter. See sections 10.3 and 10.4 in Davidson and MacKinnon (2004) for a more detailed description of the robust variance-covariance matrix.
garchFit
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, one for the mean and the other one for the variance equation. |
@method |
a string denoting the optimization method, by default the returneds string is "Max Log-Likelihood Estimation". |
@data |
a list with one entry named x, containing the data of
the time series to be estimated, the same as given by the
input argument series.
|
@fit |
a list with the results from the parameter estimation. The entries of the list depend on the selected algorithm, see below. |
@residuals |
a numeric vector with the (raw, unstandardized) residual values. |
@fitted |
a numeric vector with the fitted values. |
@h.t |
a numeric vector with the conditional variances (h.t = sigma.t^delta). |
@sigma.t |
a numeric vector with the conditional standard deviation. |
@title |
a title string. |
@description |
a string with a brief description. |
The entries of the @fit slot show the results from the
optimization.
Diethelm Wuertz for the Rmetrics R-port,
R Core Team for the 'optim' R-port,
Douglas Bates and Deepayan Sarkar for the 'nlminb' R-port,
Bell-Labs for the underlying PORT Library,
Ladislav Luksan for the underlying Fortran SQP Routine,
Zhu, Byrd, Lu-Chen and Nocedal for the underlying L-BFGS-B Routine.
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.
Bollerslev T., Wooldridge J.M. (1992); Quasi-Maximum Likelihood Estimation and Inference in Dynamic Models with Time-Varying Covariance, Econometric Reviews 11, 143–172.
Byrd R.H., Lu P., Nocedal J., Zhu C. (1995); A Limited Memory Algorithm for Bound Constrained Optimization, SIAM Journal of Scientific Computing 16, 1190–1208.
Davidson R., MacKinnon J.G. (2004); Econometric Theory and Methods, Oxford University Press, New York.
Engle R.F. (1982); Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation, Econometrica 50, 987–1008.
Nash J.C. (1990); Compact Numerical Methods for Computers, Linear Algebra and Function Minimisation, Adam Hilger.
Nelder J.A., Mead R. (1965); A Simplex Algorithm for Function Minimization, Computer Journal 7, 308–313.
Nocedal J., Wright S.J. (1999); Numerical Optimization, Springer, New York.
## UNIVARIATE TIME SERIES INPUT:
# In the univariate case the lhs formula has not to be specified ...
# A numeric Vector from default GARCH(1,1) - fix the seed:
N = 200
x.vec = as.vector(garchSim(garchSpec(rseed = 1985), n = N)[,1])
garchFit(~ garch(1,1), data = x.vec, trace = FALSE)
# An univariate timeSeries object with dummy dates:
x.timeSeries = dummyDailySeries(matrix(x.vec), units = "GARCH11")
garchFit(~ garch(1,1), data = x.timeSeries, trace = FALSE)
## Not run:
# An univariate zoo object:
x.zoo = zoo(as.vector(x.vec), order.by = as.Date(rownames(x.timeSeries)))
garchFit(~ garch(1,1), data = x.zoo, trace = FALSE)
## End(Not run)
# An univariate "ts" object:
x.ts = as.ts(x.vec)
garchFit(~ garch(1,1), data = x.ts, trace = FALSE)
## MULTIVARIATE TIME SERIES INPUT:
# For multivariate data inputs the lhs formula must be specified ...
# A numeric matrix binded with dummy random normal variates:
X.mat = cbind(GARCH11 = x.vec, R = rnorm(N))
garchFit(GARCH11 ~ garch(1,1), data = X.mat)
# A multivariate timeSeries object with dummy dates:
X.timeSeries = dummyDailySeries(X.mat, units = c("GARCH11", "R"))
garchFit(GARCH11 ~ garch(1,1), data = X.timeSeries)
## Not run:
# A multivariate zoo object:
X.zoo = zoo(X.mat, order.by = as.Date(rownames(x.timeSeries)))
garchFit(GARCH11 ~ garch(1,1), data = X.zoo)
## End(Not run)
# A multivariate "mts" object:
X.mts = as.ts(X.mat)
garchFit(GARCH11 ~ garch(1,1), data = X.mts)
## MODELING THE PERCENTUAL SPI/SBI SPREAD FROM LPP BENCHMARK:
X.timeSeries = as.timeSeries(data(LPP2005REC))
X.mat = as.matrix(x.timeSeries)
## Not run: X.zoo = zoo(X.mat, order.by = as.Date(rownames(X.mat)))
X.mts = ts(X.mat)
garchFit(100*(SPI - SBI) ~ garch(1,1), data = X.timeSeries)
# The remaining are not yet supported ...
# garchFit(100*(SPI - SBI) ~ garch(1,1), data = X.mat)
# garchFit(100*(SPI - SBI) ~ garch(1,1), data = X.zoo)
# garchFit(100*(SPI - SBI) ~ garch(1,1), data = X.mts)
## MODELING HIGH/LOW RETURN SPREADS FROM MSFT PRICE SERIES:
X.timeSeries = MSFT
garchFit(Open ~ garch(1,1), data = returns(X.timeSeries))
garchFit(100*(High-Low) ~ garch(1,1), data = returns(X.timeSeries))