Copyright 2011 - 2019 Jon Danielsson. This code is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This code is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. The GNU General Public License is available at: https://www.gnu.org/licenses/.

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, some are old and not maintained, rmgarch by Alexios Ghalanos is regularly maintained, and what is used below,

The GARCH functionality in the econometric toolbox in Matlab cannot do univariate GARCH.

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.

Last updated August 2019

```
p = read.csv('stocks.csv')
y=apply(log(p),2,diff) # calculate returns
y = y[,1:2] # consider first two stocks
y[,1] = y[,1]-mean(y[,1]) # subtract mean
y[,2] = y[,2]-mean(y[,2])
TT = dim(y)[1]
```

Last updated August 2016

```
p = csvread('stocks.csv',1,0);
p = p(:,[1,2]); % consider first two stocks
y = diff(log(p))*100; % convert prices to returns
y(:,1)=y(:,1)-mean(y(:,1)); % subtract mean
y(:,2)=y(:,2)-mean(y(:,2));
T = length(y);
```

Last updated August 2019

```
## create a matrix to hold covariance matrix for each t
EWMA = matrix(nrow=TT,ncol=3)
lambda = 0.94
S = cov(y) # initial (t=1) covar matrix
EWMA[1,] = c(S)[c(1,4,2)] # extract var and covar
for (i in 2:dim(y)[1]){
S = lambda*S+(1-lambda)* y[i-1,] %*% t(y[i-1,])
EWMA[i,] = c(S)[c(1,4,2)]
}
EWMArho = EWMA[,3]/sqrt(EWMA[,1]*EWMA[,2]) # calculate correlations
print(head(EWMArho))
print(tail(EWMArho))
```

Last updated June 2018

```
%% create a matrix to hold covariance matrix for each t
EWMA = nan(T,3);
lambda = 0.94;
S = cov(y); % initial (t=1) covar matrix
EWMA(1,:) = S([1,4,2]); % extract var and covar
for i = 2:T % loop though the sample
S = lambda*S+(1-lambda)* y(i-1,:)'*y(i-1,:);
EWMA(i,:) = S([1,4,2]); % convert matrix to vector
end
EWMArho = EWMA(:,3)./sqrt(EWMA(:,1).*EWMA(:,2)); % calculate correlations
```

Last updated August 2019

```
library(rmgarch)
spec = gogarchspec(mean.model = list(armaOrder = c(0, 0),
include.mean =FALSE),
variance.model = list(model = "sGARCH",
garchOrder = c(1,1)) ,
distribution.model = "mvnorm"
)
fit = gogarchfit(spec = spec, data = y)
show(fit)
```

Last updated August 2016

```
[par, Ht] = o_mvgarch(y,2, 1,1,1);
Ht = reshape(Ht,4,T)';
%% Ht comes from o_mvgarch as a 3D matrix, this transforms it into a 2D matrix
OOrho = Ht(:,3) ./ sqrt(Ht(:,1) .* Ht(:,4));
%% OOrho is a vector of correlations
```

Last updated August 2019

```
library(rmgarch)
xspec = ugarchspec(mean.model = list(armaOrder = c(0, 0), include.mean = FALSE))
uspec = multispec(replicate(2, xspec))
spec = dccspec(uspec = uspec, dccOrder = c(1, 1), distribution = 'mvnorm')
res = dccfit(spec, data = y)
H=res@mfit$H
DCCrho=vector(length=dim(y)[1])
for(i in 1:dim(y)[1]){
DCCrho[i] = H[1,2,i]/sqrt(H[1,1,i]*H[2,2,i])
}
```

Last updated August 2016

```
[p, lik, Ht] = dcc(y,1,1,1,1);
Ht = reshape(Ht,4,T)';
DCCrho = Ht(:,3) ./ sqrt(Ht(:,1) .* Ht(:,4));
%% DCCrho is a vector of correlations
```

Last updated August 2019

```
matplot(cbind(EWMArho,DCCrho),type='l',las=1,lty=1,col=2:3,ylab="")
mtext("Correlations",side=2,line=0.3,at=1,las=1,cex=0.8)
legend("bottomright",c("EWMA","DCC"),lty=1,col=2:3,bty="n",cex=0.7)
```

Last updated June 2018

```
plot([EWMArho,OOrho,DCCrho])
legend('EWMA','DCC','OGARCH','Location','SouthWest')
```