methods-predict: GARCH prediction function

In fGarch: Rmetrics - Autoregressive Conditional Heteroskedastic Modelling

GARCH prediction function

Description

Predicts a time series from a fitted GARCH object.

Usage

## S4 method for signature 'fGARCH'
predict(object, n.ahead = 10, trace = FALSE, mse = c("cond","uncond"),
        plot=FALSE, nx=NULL, crit_val=NULL, conf=NULL, ...)

Arguments

n.ahead	an integer value, denoting the number of steps to be forecasted, by default 10.
object	an object of class "fGARCH" as returned by the function garchFit.
trace	a logical flag. Should the prediction process be traced? By default trace=FALSE.
mse	If set to "cond", meanError is defined as the conditional mean errors \sqrt{E_t[x_{t+h}-E_t(x_{t+h})]^2}. If set to "uncond", it is defined as \sqrt{E[x_{t+h}-E_t(x_{t+h})]^2}.
plot	If set to TRUE, the confidence intervals are computed and plotted
nx	The number of observations to be plotted along with the predictions. The default is round(n*0.25), where n is the sample size.
crit_val	The critical values for the confidence intervals when plot is set to TRUE. The intervals are defined as \hat{x}_{t+h} + crit_val[2] * meanError and \hat{x}_{t+h} + crit_val[1] * meanError if two critical values are provided and \hat{x}_{t+h} \pm crit_val * meanError if only one is given. If you do not provide critical values, they will be computed automatically.
conf	The confidence level for the confidence intervals if crit_val is not provided. By default it is set to 0.95. The critical values are then computed using the conditional distribution that was chosen to create the object with garchFit using the same shape and skew parameters. If the conditionnal distribution was set to "QMLE", the critical values are computed using the empirical distribution of the standardized residuals.
...	additional arguments to be passed.

Details

The predictions are returned as a data frame with with columns "meanForecast", "meanError", and "standardDeviation". Row h contains the predictions for horizon h.

The number of records equals the number of forecasting steps n.ahead.

Value

a data frame containing 3 columns and n.ahead rows, see section ‘Details’

Author(s)

Diethelm Wuertz for the Rmetrics R-port

See Also

predict in base R

fitted, residuals,

plot, garchFit, class fGARCH,

Examples

## garchFit - 
   # Parameter Estimation of Default GARCH(1,1) Model:
   set.seed(123)
   fit = garchFit(~ garch(1, 1), data = garchSim(), trace = FALSE)
   fit

## predict -
   predict(fit, n.ahead = 10)
   predict(fit, n.ahead = 10, mse="uncond")

## predict with plotting: critical values = +/- 2

   predict(fit, n.ahead = 10, plot=TRUE, crit_val = 2)

## predict with plotting: automatic critical values 
## for different conditional distributions

  set.seed(321)
  fit2 = garchFit(~ garch(1, 1), data = garchSim(), trace=FALSE, cond.dist="sged")

## 95% confidence level
 predict(fit2, n.ahead=20, plot=TRUE) 

 set.seed(444)
 fit3 = garchFit(~ garch(1, 1), data = garchSim(), trace=FALSE, cond.dist="QMLE")

## 90% confidence level and nx=100 :
 predict(fit3, n.ahead=20, plot=TRUE, conf=.9, nx=100) 

fGarch documentation built on Oct. 15, 2023, 5:06 p.m.