This function steers the specification and estimation of GOGARCH models.
1 2 3 4 5 6  gogarch(data, formula, scale = FALSE, estby = c("ica", "mm", "ml", "nls"),
lag.max = 1, initial = NULL, garchlist = list(init.rec = "mci", delta
= 2, skew = 1, shape = 4, cond.dist = "norm", include.mean = FALSE,
include.delta = NULL, include.skew = NULL, include.shape = NULL,
leverage = NULL, trace = FALSE, algorithm = "nlminb", hessian =
"ropt", control = list(), title = NULL, description = NULL), ...)

data 
Matrix: the original data set. 
formula 
Formula: valid formula for univariate GARCH models. 
scale 
Logical, if 
estby 
Character: by fast ICA 
initial 
Numeric: starting values for optimization (used if

lag.max 
Integer: The number of used lags for computing the
matched orthogonal matrices U (used if 
garchlist 
List: Elements are passed to 
... 
Ellipsis argument: is passed to the 
The ellipsis argument is passed to the function fastICA
if
estby = "ica"
has been set, or to optim
if estby
= "nls"
is employed or to nlminb
if the GOGARCH model is
estimated by maximum likelihood, i.e., estby = "ml"
. It
is not employed if the methods of moments estimator is chosen.
If the argument initial
is left NULL
, the starting
values are computed according seq(3.0, 0.1, length.out = l)
,
whereby l
is the length of initial
for estby =
"ml"
and are set to rep(0.1, d
, whereby for
method = "nls"
. This length must be equal to m * (m 
1)/2 for estimation by MaximumLikelihood and m * (m + 1)/2 for
estimation by nonlinear leastSquares, whereby m is the number
of columns of data
.
Dependent on the chosen estimation method either an object of class
Goestica
or, Goestmm
or Goestml
or
Goestnls
is returned. All of these classes extend the
GoGARCH
class.
Bernhard Pfaff
Van der Weide, Roy (2002), GOGARCH: A Multivariate Generalized Orthogonal GARCH Model, Journal of Applied Econometrics, 17(5), 549 – 564.
Boswijk, H. Peter and van der Weide, Roy (2006), Wake me up before you GOGARCH, Tinbergen Institute Discussion Paper, TI 2006079/4, University of Amsterdam and Tinbergen Institute.
Boswijk, H. Peter and van der Weide, Roy (2009), Method of Moments Estimation of GOGARCH Models, Working Paper, University of Amsterdam, Tinbergen Institute and World Bank.
Broda, S.A. and Paolella, M.S. (2008): CHICAGO: A Fast and Accurate Method for Portfolio Risk Calculation, Swiss Finance Institute, Research Paper Series No. 0808, Zuerich.
GoGARCH
, Goestica
,
Goestmm
, Goestnls
,
Goestml
, goestmethods
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29  ## Not run:
library(vars)
## Boswijk / van der Weide (2009)
data(BVDWSTOXX)
BVDWSTOXX < zoo(x = BVDWSTOXX[, 1], order.by = BVDWSTOXX[, 1])
BVDWSTOXX < window(BVDWSTOXX, end = as.POSIXct("20071231"))
BVDWSTOXX < diff(log(BVDWSTOXX))
sectors < BVDWSTOXX[, c("AutoParts", "Banks", "OilGas")]
sectors < apply(sectors, 2, scale, scale = FALSE)
gogmm < gogarch(sectors, formula = ~garch(1,1), estby = "mm",
lag.max = 100)
gogmm
## Boswijk / van der Weide (2006)
data(BVDW)
BVDW < zoo(x = BVDW[, 1], order.by = BVDW[, 1])
BVDW < diff(log(BVDW)) * 100
gognls < gogarch(BVDW, formula = ~garch(1,1), scale = TRUE,
estby = "nls")
gognls
## van der Weide (2002)
data(VDW)
var1 < VAR(scale(VDW), p = 1, type = "const")
resid < residuals(var1)
gogml < gogarch(resid, ~garch(1, 1), scale = TRUE,
estby = "ml", control = list(iter.max = 1000))
gogml
solve(gogml@Z)
## End(Not run)

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