These Lecture series are relating the use R language software, its interface and functions required to evaluate financial risk models. Furthermore, R software applications relating financial market data, measuring risk, modern portfolio theory, risk modeling relating returns generalized hyperbolic and lambda distributions, Value at Risk (VaR) modelling, extreme value methods and models, the class of ARCH models, GARCH risk models and portfolio optimization approaches.

1. FINANCIAL RISK MGT - FRM
Lecture by;
Dr. Syed
Muhammad Ali
Tirmizi
INTRODUCTORY LECTURE
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MODERN WORLD FRM ANALYTICS
 Its absolutely true, that with modern programming
languages like R and Python, a skilled analyst can
now design their FRM analytic logic with
significantly less effort than before, using resources
such as Yahoo! or other free services for historical
quotes.

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MODERN WORLD FRM ANALYTICS
 The algorithms (A precise rule or set of rules specifying
how to solve a problem) has made us to comprehend that
the programs, using data science techniques, can not
only automate very tedious calculations but then very
positively yield insights into the human thinking level:
insights that would otherwise not be found.

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MODERN WORLD FRM ANALYTICS
 Data science involves the study of statistical and
computational models.
 Data engineering is the process of implementing
models on computers as applied to large datasets,
using files, program logic, testing, and continuous
improvement.
 We take advantage of the data science principles to
build and engineer our financial laboratory using R
Software.

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UNDERSTANDING ANALYTICAL THINKING
 As an investor, there is no more immediate feeling of
excitement than a stock split during a bull market or an
acquisition that can bid one’s stock up by 20 percent in a
day.
 Even though semi-random events can risk the desired
outcome.
 Therefore, in a nutshell, when faced with risks,
preparation makes success more feasible.

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UNDERSTANDING ANALYTICAL THINKING
 What would a financial analytics approach tell us from a
purely objective perspective? As we practice financial
analytics, we are trained, informed, and more prepared
for unexpected situations.
 Along with robust and accurate data, designing models
is a key component of the professional practice of
analytics.
 The most important applied models will be exemplified
during the course so that they can be adapted, relied
upon, and expanded.

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UNDERSTANDING ANALYTICAL THINKING
 Models are presented in this course using hand-written
code in the R language, using historical market datasets
to gain a deeper understanding of the behavior.
 Analytics are applied to Big Data in order to take
advantage of the large sample sizes.
 Insights and discovery are simply more realistically
possible with large datasets.

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WHAT IS ANALYTICAL FRM?
 The 2008 financial crisis made market practitioners to
realize that reliance on models which are mathematically
pure but fundamentally inaccurate is no longer
acceptable. Therefore, a more practical approach is
needed to tackle these situations.
 The financial markets where the instruments reside have
many more tail events (risk events – fat tails) that had
and have contributed to the flash crash, tech bubble, and
mortgage-based crisis with more to come.

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WHAT IS ANALYTICAL FRM?
 Practitioners are in need of tools for quick discovery and
simulation to complement and calibrate the mathematics.
 The emerging new field of Analytics, also known as
Data Science, is providing computational intelligence to
businesses in ways many had never envisioned.
 Analytical computer programs are recommending
everything from medical diagnoses to automobile routes
to entertainment contents.

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WHAT IS ANALYTICAL FRM?
 Investment firms like PIMCO and Vanguard have helped
investors meet retirement goals or send their children to
college by carefully delivering positive market exposure
(i.e., effective financial risk management).
 This course will provide the tools for being able to
understand better what firms like these and other
financial entities do relating financial risk management
practices.
 Therefore, Optimization is an important aspect of
financial analytics particularly in the case of FRM.

11. Statistical Analyses Using R
Starting Book for Beginners to understand
and Use R Software Effectively
11
Lecture by;
Dr. Syed
Muhammad Ali
Tirmizi

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READ THE HANDBOOK
(Book is also available online)

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INTRODUCTION
 R language has its roots at AT&T Bell
Laboratories during the 1970s and 1980s in the
S language project (Becker, Chambers, and
Wilks, 1988).
 People think that the S language would not have
been designed in the way it was if it had been
designed by computer scientists (Morandat, Hill,
Osvald, and Vitek, 2012).

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INTRODUCTION
 R is an open source variant of S developed at the
University of Auckland by Ross Ihaka and Robert
Gentleman, first appearing in 1993 (Ihaka, 1998).
 Clearly the recent popularity of R, fueled by its
open source availability and the need for statistical
and analytical computing tools, shows that the
benefits of R far outweigh the negatives.
 Overall, R is based upon the vector as a first class
item in the language.

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INTRODUCTION
 R shares this attribute with LISP, Scheme, Python,
and Matlab.
 This and the prevalence of over 4,000 publicly
available packages are two of the many strengths of
R.
 In this course, we will focus on R packages that
revolve around financial risk management
modelling and portfolio optimization.

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INTRODUCTION

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GETTING STARTED WITH R
 One of the great things about R is how easy it is to install.
 In your browser, head to the web site for the Comprehensive
R Archive Network (CRAN), http://cran.r-project.org
 This website will help you to download and install R software
for windows.
 Just as a basic test, we can create a vector of prices and plot it
with this block of code:
x <- c(1.3,1.2,1.3,NA,1.4,1.5)
plot(x,ylab="EUR prices")
is.na(x)
[1] FALSE FALSE FALSE TRUE FALSE FALSE
R
CODES

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GETTING STARTED WITH R
 The c() operator creates a vector of elements.
 This is the basic vector operator in R.
 Note the “not available” (NA) element
appearing as the fourth item of the vector.
 R’s ability to handle NAs, infinite values (Inf),
and not a number (NaN) is one of its many
strengths.

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GETTING STARTED WITH R
 A later development is the R Studio GUI available
from the web site www.rstudio.com.
 R Studio is a commercially developed GUI allowing
management of plot windows, variable contents,
and better debugging than the basic R interpreter.
 R Studio is a second-generation R user interface
with integrated code, execution, variable inspection,
and plotting windows, and expression completion.

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GETTING STARTED WITH R
 Any time a library statement is encountered, R
will check that the package is available.
 If not, it must be downloaded.
 As an example, to download the ggplot2
package, use the following command:
update.packages()
install.packages("ggplot2",dependencies=TRUE)
library(ggplot2)

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LANGUAGE FEATURES: FUNCTIONS,
ASSIGNMENT, ARGUMENTS, AND TYPES
 R has four assignment operators.
 The most basic operator is “<-”. This is the one
we use in the first assignment in the code block
below.
 R’s functional nature is so strong that even this
can be replaced by the function call assign
(“x”, 1).

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LANGUAGE FEATURES: FUNCTIONS,
ASSIGNMENT, ARGUMENTS, AND TYPES
 Over time, because people were used to other
languages that use “=” instead, and even though
“=” was used to assign parameter values in
function calls (g(x, y = 7), for example), it was
also made available for assignment in R.
 So using “<-” or “=” is now really a matter of
preference.

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LANGUAGE FEATURES: FUNCTIONS,
ASSIGNMENT, ARGUMENTS, AND TYPES
> x <- 1
> assign("x",2)
> x
[1] 2
> x = 3
> x
[1] 3
> f <-function(x)
+ {
+ x = 4
+ x
+ }
> f(x)
[1] 4
> x
[1] 3
RUN THESE
CODES for
Practice

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LANGUAGE FEATURES: FUNCTIONS,
ASSIGNMENT, ARGUMENTS, AND TYPES
 R’s fourth assignment operator, with two “<”s,
is known as the “super-assignment” operator.
 Executing it will look outside the current frame
for x, which is global to the function f , and
assign to that x.
 If there is no x in the global environment, it will
create one and assign the value to it.

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LANGUAGE FEATURES: FUNCTIONS,
ASSIGNMENT, ARGUMENTS, AND TYPES
> #The fourth type is "<<-"
> x = 3
> x
[1] 3
> f <-function(x)
+ {
+ x <<- 4
+ x
+ }
> f(x)
[1] 4
> x
[1] 4
> typeof(f)
[1] "closure"
> typeof(x)
[1] "double"
RUN THESE
CODES for
Practice

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DATA FRAMES AND INPUT– OUTPUT
 Data frames are one of R’s more unusual and handy
features.
 A data frame is a sequence of rows where the
columns are heterogeneously typed. A common way
of loading data frames is from the Excel .csv files.
#Input-ouput:
write.csv(d,file="d.txt",row.names=FALSE)
e <- read.csv("d.txt",header=TRUE)

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LISTS IN “R”
 Lists are ordered aggregates like vectors, which are
constructed by c(. . .).
 However, lists differ from the basic vectors, in that
they are recursively formed, using list(. . .).
#Lists:
c(1,c(1,2),3,"A",c(4,5))
[1] "1" "1" "2" "3" "A" "4" "5"
list(1,c(1,2),3,"A",list(4,5))

29. THANK YOU
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