fGarch (version 3010.82)

predict-methods: GARCH Prediction Function

Description

Predicts a time series from a fitted GARCH object.

Usage

"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.

Value

returns a data frame with the foloowing columns: "meanForecast", meanError, and "standardDeviation".The number of records equals the number of forecasting steps n.ahead.

Methods

object = "ANY"

Generic function.

object = "fGARCH"

Predict function for objects of 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)