rugarch (version 1.5-2)

ugarchroll-methods: function: Univariate GARCH Rolling Density Forecast and Backtesting

Description

Method for creating rolling density forecast from ARMA-GARCH models with option for refitting every n periods with parallel functionality.

Usage

ugarchroll(spec, data, n.ahead = 1, forecast.length = 500, 
n.start = NULL, refit.every = 25, refit.window = c("recursive", "moving"), 
window.size = NULL, solver = "hybrid", fit.control = list(), 
solver.control = list(), calculate.VaR = TRUE, VaR.alpha = c(0.01, 0.05), 
cluster = NULL, keep.coef = TRUE, ...)

Value

An object of class uGARCHroll.

Arguments

spec

A univariate GARCH specification object.

data

A univariate dataset, ideally with time based index.

n.ahead

The number of periods to forecast (only n.ahead=1 supported).

forecast.length

The length of the total forecast for which out of sample data from the dataset will be used for testing.

n.start

Instead of forecast.length, this determines the starting point in the dataset from which to initialize the rolling forecast.

refit.every

Determines every how many periods the model is re-estimated.

refit.window

Whether the refit is done on an expanding window including all the previous data or a moving window where all previous data is used for the first estimation and then moved by a length equal to refit.every (unless the window.size option is used instead).

window.size

If not NULL, determines the size of the moving window in the rolling estimation, which also determines the first point used.

solver

The solver to use.

fit.control

Control parameters parameters passed to the fitting function.

solver.control

Control parameters passed to the solver.

calculate.VaR

Whether to calculate forecast Value at Risk during the estimation.

VaR.alpha

The Value at Risk tail level to calculate.

cluster

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).

keep.coef

Whether to return the list of coefficients and their robust standard errors.

...

In the case of the realized GARCH (realGARCH) model, the ‘realizedVol’ is required (an xts object), and optionally the ‘n.sim’ argument indicates the samples to generate for the realized vol forecast (does not affect the 1-ahead sigma forecast).

Author

Alexios Ghalanos

Details

This is a wrapper function for creating rolling forecasts of the conditional GARCH density, and optionally calculating the Value at Risk at specified levels. The argument refit.every determines every how many periods the model is re-estimated. Given a dataset of length N, it is possible to choose either how many periods from the end to use for out of sample forecasting (using the forecast.length option), or the starting point for initializing the rolling forecast (and using all the data after that for the out of sample forecast). Only rolling 1-ahead forecasts are supported spanning the dataset, which should be useful for backtesting models. Anything more complicated should be wrapped by the user by making use of the underlying functions in the package. The function has 2 main methods for viewing the data, a standard plot method and a report methods (see class uGARCHroll for details on how to use these methods). In case of no-convergence in some of all the windows, a new method called resume now allows to pass the returned (non-converged) object with new solver and control parameters to be re-estimated (only the non-converged windows are re-estimated). Non-convergence here implies both a failure of the solver to converge to a solution (global failure) OR a failure to invert the resulting Hessian (local failure). The convergence method can be used on an object (aside from the printed warning) to print out the number of the non-converged estimation windows.
Parallel functionality is now based entirely on the parallel package, and it is up to the user to pass a cluster object, and then stop it once the routine is completed.

See Also

For specification ugarchspec, fitting ugarchfit, filtering ugarchfilter, forecasting ugarchforecast, simulation ugarchsim, parameter distribution and uncertainty ugarchdistribution, bootstrap forecast ugarchboot.

Examples

if (FALSE) {
data(sp500ret)
spec = ugarchspec(distribution.model = "std")
mod = ugarchroll(spec, data = sp500ret, n.ahead = 1, 
n.start = 1000,  refit.every = 500, refit.window = "recursive", 
solver = "hybrid", fit.control = list(),
calculate.VaR = TRUE, VaR.alpha = c(0.01, 0.025, 0.05),
keep.coef = TRUE)
report(mod, type="VaR", VaR.alpha = 0.01, conf.level = 0.95) 
report(mod, type="fpm")
}