Appendix - Introduction (in R/Python)


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.


Listing R.1: Entering and Printing Data in R
Last updated June 2018

x = 10   # assign x the value 10
print(x) # print x
		
Listing P.1: Entering and Printing Data in Python
Last updated June 2018

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

Listing R.2: Vectors, Matrices and Sequences in R
Last updated June 2018

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 P.2: Vectors, Matrices and Sequences in Python
Last updated June 2018

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
		

Listing R.3: Importing Data in R
Last updated June 2018

## 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)
		
Listing P.3: Importing Data in Python
Last updated June 2018

## 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
		

Listing R.4: Basic Summary Statistics in R
Last updated June 2018

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 P.4: Basic Summary Statistics in Python
Last updated June 2018

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
		

Listing R.5: Calculating Moments in R
Last updated June 2018

library(moments)
mean(y)          # mean
var(y)           # variance
sd(y)            # unbiased standard deviation, by default
skewness(y)      # skewness
kurtosis(y)      # kurtosis
		
Listing P.5: Calculating Moments in Python
Last updated June 2018

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
		

Listing R.6: Basic Matrix Operations in R
Last updated June 2018

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
		
Listing P.6: Basic Matrix Operations in Python
Last updated June 2018

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
		

Listing R.7: Statistical Distributions in R
Last updated June 2018

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)
		
Listing P.7: Statistical Distributions in Python
Last updated June 2018

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)
		

Listing R.8: Statistical Tests in R
Last updated June 2018

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 P.8: Statistical Tests in Python
Last updated June 2018

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
		

Listing R.9: Time Series in R
Last updated June 2018

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
		
Listing P.9: Time Series in Python
Last updated June 2018

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()
		

Listing R.10: Loops and Functions in R
Last updated June 2018

## 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)
		
Listing P.10: Loops and Functions in Python
Last updated June 2018

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))
		

Listing R.11: Basic Graphs in R
Last updated June 2018

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 P.11: Basic Graphs in Python
Last updated June 2018

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)
		

Listing R.12: Miscellaneous Useful Functions in R
Last updated June 2018

## 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))
		
Listing P.12: Miscellaneous Useful Functions in Python
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))