Chapter 2. Univariate Volatility Modeling (in R/MATLAB)


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Listing 2.1/2.2: ARCH and GARCH estimation in R
Last updated July 2020

library(rugarch)
p = read.csv('index.csv')
## We multiply returns by 100 and de-mean them
y=diff(log(p$Index))*100
y=y-mean(y)
## GARCH(1,1)
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)
## ARCH(1)
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)
##Ā tGARCH(1,1)
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
		
Listing 2.1/2.2: ARCH and GARCH estimation in MATLAB
Last updated June 2018

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)
		

Listing 2.3/2.4: Advanced ARCH and GARCH estimation in R
Last updated July 2020

## 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)
## Normal APARCH(1,1) with fixed delta
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)
		
Listing 2.3/2.4: Advanced ARCH and GARCH estimation in MATLAB
Last updated August 2016

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)