Copyright 2011, 2016, 2018 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. The latest version of the Matlab code only uses functions from Matlab toolboxes.

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.

Last updated August 2016

```
library(tseries)
library(zoo)
prices = zoo(read.csv('stocks.csv',header=TRUE,sep=','))
## convert prices of first two stocks to returns and adjust length
y1=tail(diff(log(coredata(prices[,1]))),4100)
y2=tail(diff(log(coredata(prices[,2]))),4100)
TT=length(y1)
y=cbind(y1,y2)
value = 1000 # portfolio value
p = 0.01 # probability
```

Last updated June 2018

```
using CSV;
p = CSV.read("stocks.csv",nullable=false);
## convert prices of first two stocks to returns, and adjust length
y1 = diff(log.(p[:,1]));
y2 = diff(log.(p[:,2]));
y1 = y1[length(y1)-4100+1:length(y1)];
y2 = y2[length(y2)-4100+1:length(y2)];
y = hcat(y1,y2);
T = size(y,1)
value = 1000; # portfolio value
p = 0.01; # probability
```

Last updated August 2016

```
ys = sort(y1) # sort returns
op = TT*p # p percent smallest
VaR1 = -ys[op]*value
print(VaR1)
```

Last updated June 2018

```
ys = sort(y1) # sort returns
op = convert(Int64, T*p) # p percent smallest
VaR1 = -ys[op] * value
```

Last updated 2011

```
w = matrix(c(0.3,0.7)) # vector of portfolio weights
yp = y %*% w # obtain portfolio returns
yps = sort(yp)
VaR2 = -yps[op]*value
print(VaR2)
```

Last updated June 2018

```
w = [0.3; 0.7] # vector of portfolio weights
yp = y * w # portfolio returns
yps = sort(yp)
VaR2 = -yps[op] * value
```

Last updated 2011

```
ES1 = -mean(ys[1:op])*value
print(ES1)
```

Last updated June 2018

```
ES1 = -mean(ys[1:op]) * value
```

Last updated 2011

```
sigma = sd(y1) # estimate volatility
VaR3 = -sigma * qnorm(p) * value
print(VaR3)
```

Last updated June 2018

```
sigma = std(y1) # estimate volatility
using Distributions;
VaR3 = -sigma * quantile(Normal(0,1),p) * value
```

Last updated 2011

```
sigma = sqrt(t(w) %*% cov(y) %*% w)[1] # portfolio volatility
## Note: the trailing [1] is to convert a single element matrix to float
VaR4 = -sigma * qnorm(p)*value
print(VaR4)
```

Last updated June 2018

```
sigma = sqrt(w'*cov(y)*w) # portfolio volatility
VaR4 = -sigma * quantile(Normal(0,1), p) * value
```

Last updated August 2016

```
library(QRM)
scy1=(y1)*100 # scale the returns
res=fit.st(scy1)
sigma1=res$par.ests[3]/100 # rescale the volatility
nu=res$par.ests[1]
VaR5 = - sigma1 * qt(df=nu,p=p) * value
print(VaR5)
```

Last updated June 2018

```
## using Distributions;
## res = fit_mle(TDist, y1)
## nu = res.ν (this is the Greek letter nu, not Latin v)
## sigma = sqrt(nu/(nu-2))
## VaR5 = -sigma * quantile(TDist(nu), p) * value
## Julia does not have a function for fitting Student-t data yet
## Currently: there exists Distributions.jl with fit_mle
## usage: Distributions.fit_mle(Dist_name, data[, weights])
```

Last updated June 2018

```
sigma = sd(y1)
ES2 = sigma*dnorm(qnorm(p))/p * value
print(ES2)
```

Last updated June 2018

```
sigma = std(y1)
ES2 = sigma * pdf(Normal(0,1), (quantile(Normal(0,1), p))) / p * value
```

Last updated 2011

```
VaR = -qnorm(p)
integrand = function(q){q*dnorm(q)}
ES = -sigma*integrate(integrand,-Inf,-VaR)$value/p*value
print(ES)
```

Last updated June 2018

```
using QuadGK;
VaR = -quantile(Normal(0,1), p)
integrand(x) = x*pdf(Normal(0,1), x)
ES = -sigma * quadgk(integrand, -Inf, -VaR)[1] / p * value
```

Last updated June 2018

```
WE=20
for (t in seq(TT-5,TT)){
t1=t-WE+1
window= y1[t1:t] # estimation window
sigma=sd(window)
VaR6 = -sigma * qnorm(p) * value
print(VaR6)
}
```

Last updated June 2018

```
WE = 20
for t in range(T-5, 6)
t1 = t-WE
window = y1[t1+1:t] # estimation window
sigma = std(window)
VaR6 = -sigma*quantile(Normal(0,1),p)*value
println(VaR6)
end
```

Last updated August 2016

```
lambda = 0.94;
s11 = var(y1[1:30]); # initial variance
for (t in 2:TT){
s11 = lambda * s11 + (1-lambda) * y1[t-1]^2
}
VaR7 = -qnorm(p) * sqrt(s11) * value
print(VaR7)
```

Last updated June 2018

```
lambda = 0.94
s11 = var(y1[1:30]) # initial variance
for t in range(2, T-1)
s11 = lambda * s11 + (1-lambda) * y1[t-1]^2
end
VaR7 = -sqrt(s11) * quantile(Normal(0,1), p) * value
```

Last updated 2011

```
s = cov(y) # initial covariance
for (t in 2:TT){
s = lambda*s + (1-lambda)*y[t-1,] %*% t(y[t-1,])
}
sigma = sqrt(t(w) %*% s %*% w)[1] # portfolio vol
## Note: [1] is to convert single element matrix to float
VaR8 = -sigma * qnorm(p) * value
print(VaR8)
```

Last updated June 2018

```
s = cov(y) # initial covariance
for t in range(2, T-1)
s = lambda * s + (1-lambda) * y[t-1,:] * (y[t-1,:])'
end
sigma = sqrt(w'*s*w) # portfolio vol
VaR8 = -sigma * quantile(Normal(0,1), p) * value
```

Last updated 2011

```
library(fGarch)
g = garchFit(~garch(1,1),y1,include.mean=F,trace=F)
omega = g@fit$matcoef[1,1]
alpha = g@fit$matcoef[2,1]
beta = g@fit$matcoef[3,1]
sigma2 = omega + alpha*y[TT]^2 + beta*g@h.t[TT] # calc sigma2 for t+1
VaR9 = -sqrt(sigma2) * qnorm(p) * value
print(VaR9)
```

Last updated June 2018

```
## We use the FRFGarch mini-package again (refer to Chapter 2 above)
using FRFGarch;
res = GARCHfit(y1)
sigma_fc = res.seForecast
## seForecast contains the next-day conditional volatility forecast
VaR9 = - sigma_fc * quantile(Normal(0,1), p) * value
## GARCH estimation will be slightly different from other languages
## this is due to GARCHfit choosing initial conditional vol = sample vol
```