Method for forecasting the GARCH density based on a bootstrap procedures (see details and references).
ugarchboot(fitORspec, data = NULL, method = c("Partial", "Full"),
sampling = c("raw", "kernel", "spd"), spd.options = list(upper = 0.9,
lower = 0.1, type = "pwm", kernel = "normal"), n.ahead = 10,
n.bootfit = 100, n.bootpred = 500, out.sample = 0, rseed = NA, solver = "solnp",
solver.control = list(), fit.control = list(),
external.forecasts = list(mregfor = NULL, vregfor = NULL), mexsimdata = NULL,
vexsimdata = NULL, cluster = NULL, verbose = FALSE)A uGARCHboot object containing details of the GARCH
bootstrapped forecast density.
Either a univariate GARCH fit object of class uGARCHfit or
alternatively a univariate GARCH specification object of class uGARCHspec
with valid parameters supplied via the setfixed<- function in the
specification.
Required if a specification rather than a fit object is supplied.
Either the full or partial bootstrap (see note).
Whether to sample from the raw residuals, the kernel-fitted distribution of the residuals or the spd-fitted distribution of the residuals.
If sampling is from the SPD distribution, this controls the options for fitting
this distribution to the residuals (see spd package for details).
The forecast horizon.
The number of simulation based re-fits used to generate the parameter distribution (i.e the parameter uncertainty). Not relevant for the “Partial” method.
The number of bootstrap replications per parameter distribution per n.ahead forecasts used to generate the predictive density. If this is for the partial method, simply the number of random samples from the empirical distribution to generate per n.ahead.
Optional. If a specification object is supplied, indicates how many data points to keep for out of sample testing.
A vector of seeds to initialize the random number generator for the resampling with replacement method (if supplied should be equal to n.bootfit + n.bootpred).
One of either “nlminb” or “solnp”.
Control arguments list passed to optimizer.
Control arguments passed to the fitting routine (as in the ugarchfit
method).
A list with forecasts for the external regressors in the mean and/or variance equations if specified.
List of matrices (size of list n.bootpred, with each matrix having n.ahead rows) of simulated external regressor-in-mean data. If the fit object contains external regressors in the mean equation, this must be provided else will be assumed zero.
List of matrices (size of list n.bootpred, with each matrix having n.ahead rows) of simulated external regressor-in-variance data. If the fit object contains external regressors in the mean equation, this must be provided else will be assumed zero.
A cluster object created by calling makeCluster from the parallel
package. If it is not NULL, then this will be used for parallel estimation
of the refits (remember to stop the cluster on completion).
Whether to print out progress messages.
Alexios Ghalanos
There are two main sources of uncertainty about n.ahead forecasting from GARCH
models, namely that arising from the form of the predictive density and due to
parameter estimation. The bootstrap method considered here, is based on
resampling innovations from the empirical distribution of the fitted GARCH model
to generate future realizations of the series and sigma. The “full” method,
based on the referenced paper by Pascual et al (2006), takes into account
parameter uncertainty by building a simulated distribution of the parameters
through simulation and refitting. This process, while more accurate, is very time
consuming which is why choice of parallel computation via a cluster (as in the
ugarchdistribution is available and recommended). The
“partial” method, only considers distribution uncertainty and while faster,
will not generate prediction intervals for the sigma 1-ahead forecast for which
only the parameter uncertainty is relevant in GARCH type models.
If using external regressors, the routine requires both the forecast (of length
n.ahead as in the ugarchforecast routine) and a list
of simulated forecasts as in the ugarchsim routine (else with be
assumed zero). Finally, it is possible to resample based on 3 schemes, namely
the “raw” innovations as in the original paper of Pascual et al (2006),
“kernel” fits a Gaussian kernel to the innovations from the ks
package in order to then generate random samples, and the “spd” fits
a semi-parametric distribution to the innovations based on the spd
package in order to generate the random samples, for which an optional list
(spd.options) may be further passed to the spd fitting routine.
Pascual, L., Romo, J. and Ruiz, E. 2004, Bootstrap predictive inference for
ARIMA processes, Journal of Time Series Analysis.
Pascual, L., Romo, J. and Ruiz, E. 2006, Bootstrap prediction for returns and
volatilities in GARCH models, Computational Statistics and Data Analysis.
For specification ugarchspec, fitting ugarchfit,
filtering ugarchfilter, forecasting ugarchforecast,
simulation ugarchsim, rolling forecast and estimation
ugarchroll, parameter distribution and uncertainty
ugarchdistribution.
if (FALSE) {
data(dji30ret)
spec = ugarchspec(variance.model=list(model="gjrGARCH", garchOrder=c(1,1)),
mean.model=list(armaOrder=c(1,1), arfima=FALSE, include.mean=TRUE,
archm = FALSE, archpow = 1), distribution.model="std")
ctrl = list(tol = 1e-7, delta = 1e-9)
fit = ugarchfit(data=dji30ret[, "BA", drop = FALSE], out.sample = 0,
spec = spec, solver = "solnp", solver.control = ctrl,
fit.control = list(scale = 1))
bootpred = ugarchboot(fit, method = "Partial", n.ahead = 120, n.bootpred = 2000)
bootpred
# as.data.frame(bootpred, which = "sigma", type = "q", qtile = c(0.01, 0.05))
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