gogarch | R Documentation |
This function steers the specification and estimation of GO-GARCH models.
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 GO-GARCH 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 Maximum-Likelihood and m * (m + 1)/2 for
estimation by non-linear least-Squares, 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), GO-GARCH: 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 GO-GARCH, Tinbergen Institute Discussion Paper, TI 2006-079/4, University of Amsterdam and Tinbergen Institute.
Boswijk, H. Peter and van der Weide, Roy (2009), Method of Moments Estimation of GO-GARCH 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. 08-08, Zuerich.
GoGARCH
, Goestica
,
Goestmm
, Goestnls
,
Goestml
, goest-methods
## 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("2007-12-31")) 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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