forecastGARCH: Forecast GARCH Model

In AnalyzeTS: Analyze Fuzzy Time Series

Description

The function get two object from 'Arima' class and 'garch' class, and then calculate to return forecasting answer of mean and variance of next day.

Usage

forecastGARCH(fitARMA, fitGARCH, r = 3, trace = FALSE, newxreg = NULL)

Arguments

fitARMA	A object from 'Arima' class.
fitGARCH	A object from 'garch' class.
r	Rounds the answer to the specified number of decimal places (default 3). (See round2str for details of r paramicter.)
trace	Logical. Trace optimizer output?
newxreg	A covariates value of next day for ARMAX-GARCH mdels.

Value

ARCH	GARCH coefficients.
ARMA	ARMA coefficients.
forecast	Forecasting answer: Point: forecasting time. res: forecasting residual. res^2: res square. SSL.forecast: forecating mean value. VAR.forecast: forecasting variance value.

Author(s)

Mai Thi Hong Diem <[e-mail]>

Hong Viet Minh <[e-mail]>

Examples

#Load data
library(TTR)
data(ttrc)

#Calculate SSL series
t<-ts(ttrc[,"Close"],start=1,frequency=5)
ln.t<-log(t)
r<-diff(ln.t)

#Find a ARIMA model
fit1<-arima(r,order=c(4,0,0))

#Find a GARCH model
res1<-resid(fit1)
library(tseries)
fit2<-garch(res1,order=c(2,1),trace=0)

#Forecasting
forecastGARCH(fit1,fit2,r=6,trace=TRUE)
forecastGARCH(fit1,fit2,r=6)

Example output

Loading required package: MASS
Loading required package: TSA
Loading required package: leaps
Loading required package: locfit
locfit 1.5-9.1 	 2013-03-22
Loading required package: mgcv
Loading required package: nlme
This is mgcv 1.8-20. For overview type 'help("mgcv-package")'.
Loading required package: tseries

Attaching package: 'TSA'

The following objects are masked from 'package:stats':

    acf, arima

The following object is masked from 'package:utils':

    tar

Loading required package: TTR
Loading required package: urca

Attaching package: 'AnalyzeTS'

The following object is masked from 'package:base':

    pmax

Warning message:
In garch(res1, order = c(2, 1), trace = 0) : singular information
$ARCH
          a0           a1           b1           b2 
5.611386e-06 1.040922e-01 3.563008e-01 5.149067e-01 

$ARMA
          ar1           ar2           ar3           ar4     intercept 
 0.0272990026 -0.0457375274  0.0004176592 -0.0339577069  0.0005090870 

$forecast
     Point       res    res^2 SSL.forecast VAR.forecast
1 (1110,4)                                      8.5e-05
2 (1110,5) -0.001767 3.123187                   7.8e-05
3 (1111,1)                        0.000458     7.756531

    Point SSL.forecast VAR.forecast
 (1111,1)     0.000458     7.756531

AnalyzeTS documentation built on Dec. 9, 2019, 1:07 a.m.