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 June 2018

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

Last updated June 2018

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

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```
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)
```

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```
y = [1,3,5,7,9] # lists in square brackets are stored as arrays
print(y)
print(y[2]) # 3rd element (Python indices start at 0)
print(len(y)) # as expected, y has length 5
import numpy as np
v = np.full([2,3], np.nan) # create a 2x3 matrix with NaN values
print(v)
print(v.shape) # as expected, v is size (2,3)
w=np.tile(np.transpose([1,2,3]),(3,2)) # repeats twice by rows, thrice by columns
print(w)
s = range(10) # an iterator from 0 to 9
print([x for x in s]) # return elements using list comprehension
```

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```
## There are many data sources for financial data, for instance
## Yahoo Finance, AlphaVantage and Quandl. However, some of the
## free data sources have numerous issues with accuracy and
## handling of missing data, so only CSV importing is shown here.
##
## For csv data, one can use read.csv to read it
##
## Example:
## data = read.csv('Ch1aprices.csv', header=TRUE, sep=',')
## one can use the zoo() function from the package zoo
## to turn the data into a timeseries (see Listing 1.1/1.2)
```

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```
## There are many data sources for financial data, for instance
## Yahoo Finance, AlphaVantage and Quandl. However, some of the
## free data sources have numerous issues with accuracy and
## handling of missing data, so only CSV importing is shown here.
##
## For csv data, one can use numpy.loadtxt() to read it
##
## Example:
## using numpy as np
## data = np.loadtxt('data.csv', delimiter = ',', skiprows = 1)
## skiprows=1 ensures that the header row is skipped
```

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```
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
```

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```
import numpy as np
y = [3.14,15,9.26,5]
print(sum(y)) # sum of all elements of y
print(max(y)) # maximum value of y
print(min(y)) # minimum value of y
print(np.mean(y)) # arithmetic mean
print(np.median(y)) # median
print(np.var(y)) # variance
print(np.cov(y)) # covar matrix = variance for single vector
print(np.corrcoef(y)) # corr matrix = [1] for single vector
print(np.sort(y)) # sort in ascending order
print(np.log(y)) # natural log
```

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

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```
import numpy as np
from scipy import stats
print(np.mean(y)) # mean
print(np.var(y)) # variance
print(np.std(y, ddof = 1)) # ddof = 1 for unbiased standard deviation
print(stats.skew(y)) # skewness
print(stats.kurtosis(y, fisher = False)) # fisher = False gives Pearson definition
```

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```
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
## Note: z * x is undefined since the two matrices are not conformable
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
## Note: dimensions must match along the combining axis
```

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```
import numpy as np
z = np.matrix([[1, 2], [3, 4]]) # z is a 2 x 2 matrix
x = np.matrix([1, 2]) # x is a 1 x 2 matrix
## Note: z * x is undefined since the two matrices are not conformable
print(z * np.transpose(x)) # this evaluates to a 2 x 1 matrix
b = np.concatenate((z,x), axis = 0) # "stacking" z and x vertically
print(b)
c = np.concatenate((z,np.transpose(x)), axis = 1) # "stacking" z and x horizontally
print(c)
## note: dimensions must match along the combining axis
```

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```
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)
## Similar syntax for other distributions
## q for quantile, p for cdf, d for pdf
## followed by the abbreviation of the distribution
## One can also obtain pseudorandom samples from distributions
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
## Given data, we obtain MLE estimates of distribution parameters with package MASS:
library(MASS)
res = fitdistr(x, densfun = "normal") # Fitting x to normal dist
print(res)
```

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```
import numpy as np
from scipy import stats
q = np.arange(-3,4,1) # specify a set of values
p = np.arange(0.1,1.0,0.1) # specify a set of probabilities
print(stats.norm.ppf(p)) # element-wise inverse Normal quantile
print(stats.t.cdf(q,4)) # element-wise cdf under Student-t(4)
print(stats.chi2.pdf(q,2)) # element-wise pdf under Chisq(2)
## One can also obtain pseudorandom samples from distributions using numpy.random
x = np.random.standard_t(df=5, size=100) # Sampling 100 times from TDist with 5 df
y = np.random.normal(size=50) # Sampling 50 times from a standard normal
## Given data, we obtain MLE estimates of parameters with stats:
res = stats.norm.fit(x) # Fitting x to normal dist
print(res)
```

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```
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
```

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```
from scipy import stats
from statsmodels.stats.diagnostic import acorr_ljungbox
x = np.random.standard_t(df=5, size=500) # Create dataset x
print(stats.jarque_bera(x)) # Jarque-Bera test
print(acorr_ljungbox(x, lags=20)) # Ljung-Box test
```

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```
x = rt(60, df = 5) # Create hypothetical dataset x
acf(x,20) # autocorrelation for lags 1:20
pacf(x,20) # partial autocorrelation for lags 1:20
```

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```
import statsmodels.api as sm
import matplotlib.pyplot as plt
x = np.random.standard_t(df = 5, size = 60) # Create hypothetical dataset x
q1 = sm.tsa.stattools.acf(y, nlags=20) # autocorrelation for lags 1:20
plt.bar(x = np.arange(1,len(q1)), height = q1[1:])
plt.show()
plt.close()
q2 = sm.tsa.stattools.pacf(y, nlags=20) # partial autocorr for lags 1:20
plt.bar(x = np.arange(1,len(q2)), height = q2[1:])
plt.show()
plt.close()
```

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```
## For loops
for (i in 3:7) # iterates through [3,4,5,6,7]
print(i^2)
## If-else loops
X = 10
if (X %% 3 == 0) {
print("X is a multiple of 3")
} else {
print("X is not a multiple of 3")
}
## Functions (example: a simple excess kurtosis function)
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)
```

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```
import numpy as np
## For loops
for i in range(3,8): # NOTE: range(start, end), end excluded
print(i**2) # range(3,8) iterates through [3,4,5,6,7)
## If-else loops
X = 10
if X % 3 == 0:
print("X is a multiple of 3")
else:
print("X is not a multiple of 3")
## Functions (example: a simple excess kurtosis function)
def excess_kurtosis(x, excess = 3): # note: excess optional, default = 3
m4=np.mean((x-np.mean(x))**4) # note: exponentiation in Python uses **
excess_kurt=m4/(np.std(x)**4)-excess
return excess_kurt
x = np.random.standard_t(df=5,size=60) # Create hypothetical dataset x
print(excess_kurtosis(x))
```

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```
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
```

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```
import numpy as np
import matplotlib.pyplot as plt
y = np.random.normal(size = 50)
z = np.random.standard_t(df = 4, size = 50)
## using Matplotlib to plot bar, line, histogram and scatter plots
plt.subplot(2,2,1)
plt.bar(range(len(y)), y)
plt.subplot(2,2,2)
plt.plot(y)
plt.subplot(2,2,3)
plt.hist(y)
plt.subplot(2,2,4)
plt.scatter(y,z)
```

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```
## Convert objects from one type to another with as.integer() etc
## To check type, use typeof(object)
x = 8.0
print(typeof(x))
x = as.integer(x)
print(typeof(x))
```

Last updated June 2018

```
## Convert objects from one type to another with int(), float() etc
## To check type, use type(object)
x = 8.0
print(type(x))
x = int(x)
print(type(x))
```