The original 2011 R code will not fully work on a recent R because there have been some changes to libraries. At least two R packages support estimating GARCH style models. rugarch by Alexios Ghalanos and fGarch. For our purposes there nothing to separate them but rugarch is regularly maintained, but fGarch appears not to be.
The GARCH functionality in the econometric toolbox in Matlab is trying to be too clever, but can't deliver and could well be buggy. If you want to try that, here are the docs (estimate). Besides, it can only do univariate GARCH and so can't be used in Chapter 3. Kevin Sheppard's MFE toolbox is much better, while not as user friendly, it is much better written and is certainly more comprehensive. It can be downloaded here and the documentation here is quite detailed.
library(rugarch)
p = read.csv('index.csv')
y=diff(log(p$Index))*100
y=y-mean(y)
## We multiply returns by 100 and de-mean them
spec1 = ugarchspec(variance.model = list( garchOrder = c(1, 1)),
mean.model = list( armaOrder = c(0,0),include.mean = FALSE))
res1 = ugarchfit(spec = spec1, data = y)
spec2 = ugarchspec(variance.model = list( garchOrder = c(1, 0)),
mean.model = list( armaOrder = c(0,0),include.mean = FALSE))
res2 = ugarchfit(spec = spec2, data = y)
spec3 = ugarchspec(variance.model = list( garchOrder = c(1, 1)),
mean.model = list( armaOrder = c(0,0),include.mean = FALSE),
distribution.model = "std")
res3 = ugarchfit(spec = spec3, data = y)
## plot(res) shows various graphical analysis, works in command line
p = csvread('index.csv', 1, 0);
y=diff(log(p))*100;
y=y-mean(y);
%% We multiply returns by 100 and de-mean them
tarch(y,1,0,0); % ARCH(1)
tarch(y,4,0,0); % ARCH(4)
tarch(y,4,0,1); % GARCH(4,1)
tarch(y,1,0,1); % GARCH(1,1)
tarch(y,1,0,1,'STUDENTST'); % t-GARCH(1,1)
library(rugarch)
## normal APARCH(1,1)
spec4 = ugarchspec(variance.model = list(model="apARCH", garchOrder = c(1, 1)),
mean.model = list( armaOrder = c(0,0),include.mean = FALSE))
res4 = ugarchfit(spec = spec4, data = y)
#show(res4)
spec5 = ugarchspec(variance.model = list(model="apARCH", garchOrder = c(1, 1)),
mean.model = list( armaOrder = c(0,0),include.mean = FALSE), fixed.pars=list(delta=2))
res5 = ugarchfit(spec = spec5, data = y)
show(res5)
aparch(y,1,1,1); % APARCH(1,1)
aparch(y,2,2,1); % APARCH(2,1)
aparch(y,1,1,1,'STUDENTST'); % t-APARCH(1,1)