rugarch (version 1.2-2)

ugarchfit-methods: function: Univariate GARCH Fitting

Description

Method for fitting a variety of univariate GARCH models.

Usage

ugarchfit(spec, data, out.sample = 0, solver = "solnp", solver.control = list(), 
fit.control = list(stationarity = 1, fixed.se = 0, scale = 0, rec.init = 'all'), ...)

Arguments

data

A univariate data object. Can be a numeric vector, matrix, data.frame, zoo, xts, timeSeries, ts or irts object.

spec

A univariate GARCH spec object of class uGARCHspec.

out.sample

A positive integer indicating the number of periods before the last to keep for out of sample forecasting (see details).

solver

One of either nlminb, solnp, lbfgs, gosolnp, nloptr or hybrid (see notes).

solver.control

Control arguments list passed to optimizer.

fit.control

Control arguments passed to the fitting routine. Stationarity explicitly imposes the variance stationarity constraint during optimization. The fixed.se argument controls whether standard errors should be calculated for those parameters which were fi

...

For the multiplicative component sGARCH model (mcsGARCH), the additional argument DailyVar is required and should be an xts object of the daily forecasted variance to use with the intraday data.

Value

• A uGARCHfit object containing details of the GARCH fit.

Details

The GARCH optimization routine first calculates a set of feasible starting points which are used to initiate the GARCH recursion. The main part of the likelihood calculation is performed in C-code for speed. The out.sample option is provided in order to carry out forecast performance testing against actual data. A minimum of 5 data points are required for these tests. If the out.sample option is positive, then the routine will fit only N - out.sample (where N is the total data length) data points, leaving out.sample points for forecasting and testing using the forecast performance measures. In the ugarchforecast routine the n.ahead may also be greater than the out.sample number resulting in a combination of out of sample data points matched against actual data and some without, which the forecast performance tests will ignore. The gosolnp solver allows for the initialization of multiple restarts of the solnp solver with randomly generated parameters (see documentation in the Rsolnp-package for details of the strategy used). The solver.control list then accepts the following additional (to the solnp) arguments: n.restarts is the number of solver restarts required (defaults to 1), parallel (logical), pkg (either snowfall or multicore) and cores (the number of cores or workers to use) for use of parallel functionality, rseed is the seed to initialize the random number generator, and n.sim is the number of simulated parameter vectors to generate per n.restarts. The hybrid strategy solver first tries the solnp solver, in failing to converge then tries then nlminb, the gosolnp and finally the nloptr solvers. Solver control parameters can be passed for all the solvers in the solver.control list as one long list which will be filtered for each solver's specific options as and when that solver is called during the hybrid strategy optimization. It is still possible that the Hessian at the optimal found cannot be inverted, in which case a warning is printed and there will not be any standard errors. In this case it is suggested that the problem is re-run with different solver parameters. It is also possible that the solution, while still almost optimal may be at a saddle-point very near the global optimum in which case the Hessian may still be invertible but one eigenvalue is negative. The uGARCHfit object has a value in the fit slot called condH (object@fit$condH) which indicates the approximate number of decimal places lost to roundoff/numerical estimation error. When this is NaN, this indicates the case just described of one negative eigenvalue/saddlepoint (this previously flagged a warning but is now silenced and it is upto to the user to decide whether it is worth investigating further).

See Also

For specification ugarchspec,filtering ugarchfilter, forecasting ugarchforecast, simulation ugarchsim, rolling forecast and estimation ugarchroll, parameter distribution and uncertainty ugarchdistribution, bootstrap forecast ugarchboot.

Examples

# Basic GARCH(1,1) Spec
data(dmbp)
spec = ugarchspec()
fit = ugarchfit(data = dmbp[,1], spec = spec)
fit
coef(fit)
head(sigma(fit))
#plot(fit,which="all")
# in order to use fpm (forecast performance measure function)
# you need to select a subsample of the data:
spec = ugarchspec()
fit = ugarchfit(data = dmbp[,1], spec = spec, out.sample=100)
forc = ugarchforecast(fit, n.ahead=100)
# this means that 100 data points are left from the end with which to
# make inference on the forecasts
fpm(forc)