# Appendix - Introduction

### R and Julia

Copyright 2011 - 2023 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: www.gnu.org/licenses.

##### Listing 0.1

##### Entering and Printing Data in R

```
x = 10 # assign x the value 10
print(x) # print x
```

##### Listing 0.1

##### Entering and Printing Data in Julia

```
x = 10 # assign x the value 10
println(x) # print x
```

##### Listing 0.2

##### Vectors, Matrices and Sequences in R

```
y = c(1,3,5,7,9) # create vector using c()
print(y)
print(y[3]) # calling 3rd element (R indices start at 1)
print(dim(y)) # gives NULL since y is a vector, not a matrix
print(length(y)) # as expected, y has length 5
v = matrix(nrow=2,ncol=3) # fill a 2 x 3 matrix with NaN values (default)
print(dim(v)) # as expected, v is size (2,3)
w = matrix(c(1,2,3),nrow=6,ncol=3) # repeats matrix twice by rows, thrice by columns
print(w)
s = 1:10 # s is a list of integers from 1 to 10 inclusive
print(s)
```

##### Listing 0.2

##### Vectors, Matrices and Sequences in Julia

```
y = [1,3,5,7,9] # lists in square brackets are stored as arrays
println(y)
println(y[3]) # calling 3rd element (Julia indices start at 1)
println(size(y)) # size of y
println(length(y)) # as expected, y has length 5
v = fill!(Matrix{Float64}(undef, 2,3),NaN) # 2x3 Float64 matrix of NaNs - computationally better
v = fill(NaN, (2,3)) # 2x3 Float64 matrix of NaNs - direct
println(v) # Julia prints matrices in a single line
println(size(v)) # as expected, v is size (2,3)
w = repeat([1,2,3]', outer = [3,2]) # repeats matrix thrice by rows, twice by columns
println(w)
s = 1:10 # s is an sequence which one can loop across
println(collect(s)) # return sequence elements as an array
```

##### Listing 0.3

##### Basic Summary Statistics in R

```
y=matrix(c(3.1,4.15,9))
sum(y) # sum of all elements of y
prod(y) # product of all elements of y
max(y) # maximum value of y
min(y) # minimum value of y
range(y) # min, max value of y
mean(y) # arithmetic mean
median(y) # median
var(y) # variance
cov(y) # covar matrix = variance for single vector
cor(y) # corr matrix = [1] for single vector
sort(y) # sorting in ascending order
log(y) # natural log
```

##### Listing 0.3

##### Basic Summary Statistics in Julia

```
y = [3.14,15,9.26,5]
using Statistics; # load package needed
println("sum: ", sum(y)) # return sum of all elements of y
println("product: ", prod(y)) # return product of all elements of y
println("max: ", maximum(y)) # return maximum value of y
println("min: ", minimum(y)) # return minimum value of y
println("mean: ", mean(y)) # arithmetic mean
println("median: ", median(y)) # median
println("variance: ", var(y)) # variance
println("cov_matrix: ", cov(y)) # covar matrix = variance for single vector
println("cor_matrix: ", cor(y)) # corr matrix = [1] for single vector
println(sort(y)) # sorts y in ascending order
println(log.(y)) # natural log, note . denotes elementwise operation
```

##### Listing 0.4

##### Calculating Moments in R

```
library(moments)
mean(y) # mean
var(y) # variance
sd(y) # unbiased standard deviation, by default
skewness(y) # skewness
kurtosis(y) # kurtosis
```

##### Listing 0.4

##### Calculating Moments in Julia

```
using StatsBase;
println("mean: ", mean(y)) # mean
println("variance: ", var(y)) # variance
println("std dev: ", std(y)) # unbiased standard deviation
println("skewness: ", skewness(y)) # skewness
println("kurtosis: ", kurtosis(y)) # EXCESS kurtosis (note the different default)
```

##### Listing 0.5

##### Basic Matrix Operations in R

```
z = matrix(c(1,2,3,4),2,2) # z is a 2 x 2 matrix
x = matrix(c(1,2),1,2) # x is a 1 x 2 matrix
z %*% t(x) # this evaluates to a 2 x 1 matrix
rbind(z,x) # "stacking" z and x vertically
cbind(z,t(x)) # "stacking z and x' horizontally
```

##### Listing 0.5

##### Basic Matrix Operations in Julia

```
z = Matrix([[1 2];[3 4]]) # z is a 2 x 2 matrix
x = Matrix([1 2]) # x is a 1 x 2 matrix
println(z * x') # this evaluates to a 2 x 1 matrix
b = vcat(z,x) # "stacking" z and x vertically
c = hcat(z,x') # "stacking" z and x' horizontally
```

##### Listing 0.6

##### Statistical Distributions in R

```
q = seq(from = -3, to = 3, length = 7) # specify a set of values
p = seq(from = 0.1, to = 0.9, length = 9) # specify a set of probabilities
qnorm(p, mean = 0, sd = 1) # element-wise inverse Normal quantile
pt(q, df = 4) # element-wise cdf under Student-t(4)
dchisq(q, df = 2) # element-wise pdf under Chisq(2)
x = rt(100, df = 5) # Sampling 100 times from TDist with 5 df
y = rnorm(50, mean = 0, sd = 1) # Sampling 50 times from a standard normal
library(MASS)
res = fitdistr(x, densfun = "normal") # Fitting x to normal dist
print(res)
```

##### Listing 0.6

##### Statistical Distributions in Julia

```
using Distributions;
q = collect((-3:1:3)) # specify a set of values
p = collect((0.1:0.1:0.9)) # specify a set of probabilities
println(quantile.(Normal(0,1),p)) # element-wise inverse Normal quantile
println(cdf.(TDist(4), q)) # element-wise cdf calculation under Student-t(4)
println(pdf.(Chisq(2), q)) # element-wise pdf calculation under Chisq(2)
x = rand(TDist(5), 100) # Sampling 100 times from TDist with 5 df
y = rand(Normal(0,1), 50) # Sampling 50 times from a standard normal
fit_mle(Normal, x) # Fitting x to normal dist
```

##### Listing 0.7

##### Statistical Tests in R

```
library(tseries)
x = rt(500, df = 5) # Create hypothetical dataset x
jarque.bera.test(x) # Jarque-Bera test for normality
Box.test(x, lag = 20, type = c("Ljung-Box")) # Ljung-Box test for serial correlation
```

##### Listing 0.7

##### Statistical Tests in Julia

```
using Random, HypothesisTests; # loading required packages
Random.seed!(100) # set random seed
x = rand(TDist(5), 500) # create hypothetical dataset x
println(JarqueBeraTest(x)) # Jarque-Bera test for normality
println(LjungBoxTest(x,20)) # Ljung-Box test for serial correlation
```

##### Listing 0.8

##### Time Series in R

```
x = rt(60, df = 5) # Create hypothetical dataset x
par(mfrow=c(1,2), pty='s')
acf(x,20) # autocorrelation for lags 1:20
pacf(x,20) # partial autocorrelation for lags 1:20
```

##### Listing 0.8

##### Time Series in Julia

```
using Plots;
Random.seed!(100)
x = rand(TDist(5), 60) # Create hypothetical dataset x
acf = autocor(x, 1:20) # autocorrelation for lags 1:20
pacf = autocor(x, 1:20) # partial autocorrelation for lags 1:20
plot(bar(acf, title = "Autocorrelation", legend = false))
plot(bar(pacf, title = "Partial autocorrelation", legend = false))
```

##### Listing 0.9

##### Loops and Functions in R

```
for (i in 3:7) # iterates through [3,4,5,6,7]
print(i^2)
X = 10
if (X %% 3 == 0) {
print("X is a multiple of 3")
} else {
print("X is not a multiple of 3")
}
excess_kurtosis = function(x, excess = 3){ # note: excess optional, default=3
m4 = mean((x-mean(x))^4)
excess_kurt = m4/(sd(x)^4) - excess
excess_kurt
}
x = rt(60, df = 5) # Create hypothetical dataset x
excess_kurtosis(x)
```

##### Listing 0.9

##### Loops and Functions in Julia

```
for i in range(3,length = 5) # using range with the "length" option
println(i^2) # where n = number of terms
end # this iterates over [3,4,5,6,7]
X = 10
if X % 3 == 0
println("X is a multiple of 3")
else
println("X is not a multiple of 3")
end
using Statistics;
function excess_kurtosis(x, excess = 3)::Float64 # excess optional, default = 3
m4 = mean((x .- mean(x)).^4) # element-wise exponentiation .^
excess_kurt = m4/(std(x)^4) - excess
return excess_kurt
end
using Random, Distributions;
Random.seed!(100)
x = rand(TDist(5), 60) # Create hypothetical dataset x
excess_kurtosis(x)
```

##### Listing 0.10

##### Basic Graphs in R

```
y = rnorm(50, mean = 0, sd = 1)
par(mfrow=c(2,2)) # sets up space for subplots
barplot(y) # bar plot
plot(y,type='l') # line plot
hist(y) # histogram
plot(y) # scatter plot
```

##### Listing 0.10

##### Basic Graphs in Julia

```
y = rand(Normal(0,1), 50)
return plot(bar(y, title = "Bar plot"), plot(y, title = "Line plot"),
histogram(y, title = "Histogram"), scatter(y, title = "Scatter plot"), legend = false)
```

##### Listing 0.11

##### Miscellaneous Useful Functions in R

```
x = 8.0
print(typeof(x))
x = as.integer(x)
print(typeof(x))
```

##### Listing 0.11

##### Miscellaneous Useful Functions in Julia

```
x = 8.0
println(typeof(x))
x = convert(Int, 8.0)
println(typeof(x))
y = rand(Normal(0,1), 100)
res = fit_mle(Normal, y)
println("Fitted mean: ", res.μ)
println("Fitted sd: ", res.σ)
```

##### Financial Risk Forecasting

Market risk forecasting with R, Julia, Python and Matlab. Code, lecture slides, implementation notes, seminar assignments and questions.© All rights reserved, Jon Danielsson,