*DRAFT 1.3*

*William G. Foote*

*2018-01-09*

Science alone of all the subjects contains within itself the lesson of the danger of belief in the infallibility of the greatest teachers of the preceding generation. - Richard Feynman

This book is designed to provide students, analysts, and practitioners (the collective “we” and “us”) with approaches to analyze various types of financial data sets, and to make meaningful decisions based on statistics obtained from the data. The book covers various areas in the financial industry, from analyzing credit data (credit card receivables), to studying global relations between macroeconomic events, to managing risk and return in multi-asset portfolios. The topics in the book employ a wide range of techniques including non-linear estimation, portfolio analytics, risk measurement, extreme value analysis, forecasting and predictive techniques, and financial modeling.

## 1.1 Analytics

By its very nature the science of data analytics is disruptive. That means, among many other things, that much attention should be paid to the scale and range of invalid, as yet not understood, outlying, and emerging trends. This is as true within the finance domain of knowledge as any other.

Throughout the book, we will learn the core of ideas of programming software development to implement financial analyses (functions, objects, data structures, flow control, input and output, debugging, logical design and abstraction) through writing code. We will learn how to set up stochastic simulations, manage data analyses, employ numerical optimization algorithms, diagnose their limitations, and work with and filter large data sets. Since code is also an important form of communication among analysts, we will learn how to comment and organize code, as well as document work product.

## 1.2 Chapter Outline

Here is an outline of topics covered by chapter.

**2. R Warm-Ups for Finance.** `R`

computations, data structures, financial, probability, and statistics calculations, visualization. Documentation with `R Markdown`

.

**3. More R Warm-Ups.** Functions, loops, control bootstrapping, simulation, and more visualization.

**4. Stylized Facts of Financial Markets.** Data from FRED, Yahoo, and other sources. Empirical characteristics of economic and financial time series. Boostrapping confidence intervals.

**5. Term Structure of Interest Rates.** Bond pricing, forward and yield curves. Estimating Non-linear regression splines. Applications.

**6. Market Risk.** Quantile (i.e., Value at Risk) and coherent (i.e., Expected Shortfall) risk measures. **7. Credit Risk.** Hazard rate models, Markov transition probabilities Risk measures, Laplace simulation with FFT.

**8. Operational Risk and Extreme Finance.** Generate frequency and severity of operational loss distributions. Estimating operational risk distribution parameters. Simulating loss distributions.

**9. Measuring Volatility.** Measuring volatility. GARCH estimation. GARCH simulation. Measuring Value at Risk (VaR) and Expected Shortfall (ES).

**10. Portfolio Optimization.** Combining risk management with portfolio allocations. Optimizing allocations. Simulating the efficient frontier.

**11. Aggregating Enterprise Risks.** Enterprise risk management analytics and application. Workflow to build an online application. Introduction to `Shiny`

and `ShinyDashboard`

. Building a simple app. Using `R Markdown`

and `Shiny`

.

## 1.3 Setting Up R for Analytics

### 1.3.1 Why this Appendix?

The general aim of this appendix is to situate the software platform `R`

as part of your learning of statistics, operational research, and data analytics that accompanies nearly every domain of knowledge, from epidemiology to financial engineering. The specific aim of this appendix is to provide detailed instructions on how to install `R`

an integrated development environment (IDE), `RStudio`

, and a documentation system `R Markdown`

on a personal computing platform (also known as your personal computer). This will enable us to learn the statistical concepts usually included in an analytics course with explanations and examples aimed at the appropriate level. This appendix purposely does not attempt to teach you about `R`

’s many fundamental and advanced features.

### 1.3.2 Some useful `R`

resources

There are many `R`

books useful for managing implementation of models in this course. Three useful `R`

books include:

- Paul Teetor,
*The R Cookbook* - Phil Spector,
*Data Manipulation with R* - Norman Matloff,
*The Art of R Programming: A Tour of Statistical Software Design* - John Taveras,
*R for Excel Users*at https://www.rforexcelusers.com/book/.

The first one will serve as our `R`

textbook. The other books are extremely valuable reference works. You will ultimately need all three (and whatever else you can get your hands on) in your professional work. John Taveras’s book is an excellent bridge and compendium of Excel and R practices.

Much is available in books, e-books, and online for free. This is an extensive online user community that links expert and novice modelers globally.

The standard start-up is at CRAN http://cran.r-project.org/manuals.html. A script in the appendix can be dropped into a workspace and played with easily.

Julian Faraway’s https://cran.r-project.org/doc/contrib/Faraway-PRA.pdf is a fairly complete course on regression where you can imbibe deeply of the many ways to use

`R`

in statistics.Along econometrics lines is Grant Farnsworth’s https://cran.r-project.org/doc/contrib/Farnsworth-EconometricsInR.pdf.

Winston Chang’s http://www.cookbook-r.com/ and Hadley Wickham’s example at http://ggplot2.org/ are online graphics resources.

Stack Overflow is a programming user community with an

`R`

thread at http://stackoverflow.com/questions/tagged/r. The odds are that if you have a problem, error, or question, it has already been asked, and answered, on this site.For using

`R Markdown`

there is a short reference at https://www.rstudio.com/wp-content/uploads/2015/03/rmarkdown-reference.pdf. Cosma Shalizi has a much more extensive manual at http://www.stat.cmu.edu/~cshalizi/rmarkdown/.

### 1.3.3 Install `R`

on your computer

Directions exist at the `R`

website, http://cran.r-project.org/ for installing `R`

. There are several `twotorials`

, including some on installation that can be helpful at http://www.twotorials.com/.

Here are more explicit instructions that tell you what to do.

Download the software from the CRAN website. There is only one file that you need to obtain (a different file depending on the operating system). Running this file begins the installation process which is straight-forward in most, if not all, systems.

Download

`R`

from the web. Go the`R`

home page at http://cran.r-project.org/.If you have

**Windows**(95 or later), then perform these actions. Click on the link`Windows (95 and later)`

, then click on the link called`base`

, and finally click on the most recent executable version. After the download is complete, double-click on the downloaded file and follow the on screen installation instructions. If you have a 64 bit system, use the 64 bit version of`R`

.If you have

**Macintosh**(OS X), then perform these actions. Click on the link`MacOS (System 8.6 to 9.1 and MacOS X)`

, then click on the most recent package which begins the download. When given a choice to unstuff or save, choose save and save it on your desktop. Double-click on the downloaded file. Your Mac will unstuff the downloaded file and create an`R`

folder. Inside this folder, there are many files including one with the`R`

logo. You may drag a copy of this to your panel and then drag the whole`R`

folder to your`Applications`

folder (located on the hard drive). After completing this, you can drag the original downloaded file to your trash bin.

### 1.3.4 Install `RStudio`

Every software platform has a graphical user interface (“GUI” for short). One of the more popular GUIs, and the one used exclusively in this course, is provided by `RStudio`

at http://www.rstudio.com. `RStudio`

is a freely distributed integrated development environment (IDE) for `R`

. It includes a console to execute code, a syntax-highlighting editor that supports direct code execution, as well as tools for plotting, reviewing code history, debugging code, and managing workspaces. In the following steps you will navigate to the `RStudio`

website where you can download `R`

and `RStudio`

. These steps assume you have a `Windows`

or `Mac OSX`

operating system.

Click on https://www.rstudio.com/products/RStudio/ and navigate down to the

`Download Desktop`

button and click.Click on the

`Download`

button for the`RStudio Desktop Personal License`

choice.Navigate to the sentence: “RStudio requires R 2.11.1+. If you don’t already have R, download it

*here*.” If you have not downloaded`R`

(or want to again), click on`here`

. You will be directed to the https://cran.rstudio.com/ website in a new browser tab.

In the

`CRAN`

site, click on`Download R for Windows`

, or`Download R for (MAC) OS X`

depending on the computer you use. This action sends you to a new webpage in the site.Click on

`base`

. This action takes you to the download page itself.

- If you have
`Windows`

Click on

`Download R 3.3.2 for Windows (62 megabytes, 32/64 bit)`

(as of 11/8/2016; other version numbers may appear later than this date). A Windows installer in an over 70 MB`R-3.3.2-win.exe`

file will download through your browser.In the Chrome browser, the installation-executable file will reside in a tray at the bottom of the browser. Click on the up arrow to the right of the file name and click

`Open`

in the list box. Follow the many instructions and accept default values throughout.Use the default

`Core`

and`32-Bit`

files if you have a Windows 32-bit Operating System. You may want to use`64-Bit`

files if that is your operating system architecture. You can check this out by going to the`Control Panel`

, then`System and Security`

, then`System`

, and look up the`System Type:`

. It may read for example`32-bit Operating System`

.Click

`Next`

to accept defaults. Click`Next`

again to accept placing`R`

in the startup menu folder. Click`Next`

again to use the`R`

icon and alter and create registries. At this point the installer extracts files, creates shortcuts, and completes the installation.Click

`Finish`

to finish.

- If you have a
`MAC OS X`

Click on

`Download R 3.3.2 for MACs (62 megabytes, 32/64 bit)`

(as of 11/8/2016; other version numbers may appear later than this date). A Windows installer in an over 70 MB`R-3.3.2-win.exe`

file will download through your browser.When given a choice to unstuff or save, choose save and save it on your desktop. Double-click on the downloaded file. Your Mac will unstuff the downloaded file and create an

`R`

folder. Inside this folder, there are many files including one with the`R`

logo.Inside the

`R`

folder drag a copy of`R`

logo file to your panel and then drag the whole`R`

folder to your`Applications`

folder (located on the hard drive).

- Now go back to
`RStudio`

browser tab. Click on`RStudio 1.0.44 - Windows Vista/7/8/10`

or`RStudio 1.0.44 - MAC OS X`

to download`RStudio`

. Executiable files will download. Follow the directions exactly, and similarly, to the ones above.

### 1.3.5 Install `R Markdown`

Click on `RStudio`

in your tray or start up menu. Be sure you are connected to the Internet. A console panel will appear. At the console prompt `>`

type

`install.packages("rmarkdown")`

This action will install the

`RMarkdown`

package. This package will enable you to construct documentation for your work in the course. Assignments will be documented using RMarkdown for submission to the learning management system.This extremely helpful web page, http://rmarkdown.rstudio.com/gallery.html, is a portal to several examples of

`R Markdown`

source files that can be loaded into`RStudio`

, modified, and used with other content for your own work.

### 1.3.6 Install LaTex

`R Markdown`

uses a text rendering system called `LaTeX`

to render text, including mathematical and graphical content.

- Install the
`MikTeX`

document rendering system for`Windows`

or`MacTeX`

document rendering system for`Mac OS X`

.

For

`Windows`

, navigate to the https://miktex.org/download page and go to the**64- or 32- bit**installer. Click on the appropriate`Download`

button and follow the directions.**Be very sure you select the**. Frequently Asked Questions (FAQ) can be found at https://docs.miktex.org/faq/. If you have*COMPLETE*installation`RStudio`

already running, you will have to restart your session.For

`MAC OS X`

, navigate to the http://www.tug.org/mactex/ page and download the`MacTeX`

system and follow the directions. This distribution requires Mac OS 10.5 Leopard or higher and runs on Intel or PowerPC processors.**Be very sure you select the**. Frequently Asked Questions (FAQ) can be found at https://docs.miktex.org/faq/. If you have*FULL*installation`RStudio`

already running, you will have to restart your session. FAQ can be found at http://www.tug.org/mactex/faq/index.html.

### 1.3.7 R Markdown

Open `RStudio`

and see something like this screenshot…

You can modify the position and content of the four panes by selecting

`View > Panes > Pane Options`

.Under

`File > New File > Rmarkdown`

a dialog box invites you to open document, presentation, Shiny, and other files. Upon choosing`documents`

you may open up a new file. Under`File > Save As`

save the untitle file in an appropriate directory. The`R Markdown`

file extension`Rmd`

will appear in the file name in your directory.When creating a new

`Rmarkdown`

file,`RStudio`

deposits a template that shows you how to use the markdown approach. You can generate a document by clicking on`knit`

in the icon ribbon attached to the file name tab in the script pane. If you do not see`knit`

, then you might need to install and load the`knitr`

package with the following statements in the`R`

console. You might need also to restart your RStudio session.

`install.packages("knitr")library(knitr)`

The `Rmd`

file contains three types of content:

- An (optional)
**YAML header**surrounded by`---`

on the top and the bottom of`YAML`

statements.`YAML`

is “Yet Another Markdown (or up) Language”. Here is an example from this document:

`---title: "Setting Up R for Analytics"author: "Bill Foote"date: "November 11, 2016"output: pdf_document---`

**Chunks**of R code surrounded by ``` (find this key usually with the`~`

symbol).- Text mixed with text formatting like
`# heading`

and`_italics_`

and mathematical formulae like`$z = \frac{(\bar x-\mu_0)}{s/\sqrt{n}}$`

which will render

\[z = \frac{(\bar x-\mu_0)}{s/\sqrt{n}}\].

When you open an `.Rmd`

file, `RStudio`

provides an interface where code, code output, and text documentation are interleaved. You can run each code chunk by clicking the `Run`

icon (it looks like a play button at the top of the chunk), or by pressing `Cmd/Ctrl + Shift + Enter`

. `RStudio`

executes the code and displays the results in the console with the code.

You can write mathematical formulae in an `R Markdown`

document as well. For example, here is a formula for net present value.

`$$NPV = \sum_{t=0}^{T} \frac{NCF_t}{(1+WACC)^t}$$`

This script will render

\[NPV = \sum_{t=0}^{T} \frac{NCF_t}{(1+WACC)^t}\]

- Here are examples of common in file text formatting in
`R Markdown`

.

`Text formatting ------------------------------------------------------------*italic* or _italic_**bold** __bold__`code`superscript^2^ and subscript~2~Headings------------------------------------------------------------# 1st Level Header## 2nd Level Header### 3rd Level HeaderLists------------------------------------------------------------* Bulleted list item 1* Item 2 * Item 2a * Item 2b1. Numbered list item 11. Item 2. The numbers are incremented automatically in the output.Links and images------------------------------------------------------------<http://example.com>[linked phrase](http://example.com)![optional caption text](path/to/img.png)Tables ------------------------------------------------------------First Header | Second Header------------- | -------------Content Cell | Content CellContent Cell | Content CellMath------------------------------------------------------------$\frac{\mu}{\sigma^2}$\[\frac{\mu}{\sigma^2}]`

More information can be found at the R Markdown web site.

### 1.3.8 jaRgon

(directly copied from Patrick Burns at http://www.burns-stat.com/documents/tutorials/impatient-r/jargon/, and annotated a bit, for educational use only.)

- atomic vector

An object that contains only one form of data. The atomic modes are: *logical*, *numeric*, *complex* and *character*.

- attach

The act of adding an item to the search list. You usually attach a package with the `require`

function, you attach saved files and objects with the `attach`

function.

- data frame

A rectangular data object where each column may be a different type of data. Conceptually a generalization of a matrix, but implemented entirely differently.

- factor

A data object that represents categorical data. It is possible (and often unfortunate) to confuse a factor with a character vector.

- global environment

The first location on the search list, and the place where objects that you create reside. See **search list**.

- list

A type of object with possibly multiple components where each component may be an arbitrary object, including a list.

- matrix

A rectangular data object where all cells have the same data type. Conceptually a specialization of a data frame, but implemented entirely differently. This object has rows and columns.

- package

A collection of `R`

objects in a special format that includes help files and such. Most packages primarily or exclusively contain functions, but some packages exclusively contain datasets.

- search list

The collection of locations that `R`

searches for objects when it is evaluating a command.

## 1.4 Nomenclature

Here is a table of symbols used throughout the text.

As an expert and enthusiast, I have access to a vast amount of information and can provide insights on various topics. In this case, I can provide information related to the concepts mentioned in the article you provided. Let's dive into it!

#### Financial Data Analytics

The book you mentioned is designed to provide students, analysts, and practitioners with approaches to analyze various types of financial data sets and make meaningful decisions based on statistics obtained from the data. It covers various areas in the financial industry, including credit data analysis, macroeconomic event analysis, risk management, and portfolio optimization [[1]].

#### Analytics

Data analytics is a disruptive field that requires attention to the scale and range of invalid, outlying, and emerging trends. This applies to the finance domain as well. The book emphasizes the core ideas of programming software development to implement financial analyses, including functions, objects, data structures, flow control, input and output, debugging, logical design, and abstraction through writing code. It also covers stochastic simulations, data analysis management, numerical optimization algorithms, and working with large data sets [[2]].

#### Chapter Outline

The book is divided into several chapters covering different topics. Here is an outline of the topics covered in each chapter:

- R Warm-Ups for Finance: R computations, data structures, financial, probability, and statistics calculations, visualization. Documentation with R Markdown.
- More R Warm-Ups: Functions, loops, control bootstrapping, simulation, and more visualization.
- Stylized Facts of Financial Markets: Empirical characteristics of economic and financial time series, bootstrapping confidence intervals.
- Term Structure of Interest Rates: Bond pricing, forward and yield curves, estimating non-linear regression splines.
- Market Risk: Quantile (Value at Risk) and coherent (Expected Shortfall) risk measures.
- Credit Risk: Hazard rate models, Markov transition probabilities, risk measures, Laplace simulation with FFT.
- Operational Risk and Extreme Finance: Generating frequency and severity of operational loss distributions, estimating operational risk distribution parameters, simulating loss distributions.
- Measuring Volatility: GARCH estimation, GARCH simulation, measuring Value at Risk (VaR) and Expected Shortfall (ES).
- Portfolio Optimization: Combining risk management with portfolio allocations, optimizing allocations, simulating the efficient frontier.
- Aggregating Enterprise Risks: Enterprise risk management analytics and application, building an online application using Shiny and ShinyDashboard.
- Setting Up R for Analytics: This chapter provides detailed instructions on how to install R, RStudio (an integrated development environment for R), and R Markdown on a personal computer. It also mentions some useful resources for learning R [[3]].

#### Setting Up R for Analytics

Chapter 1.3 of the book focuses on setting up R for analytics. It provides detailed instructions on how to install R, RStudio, and R Markdown on a personal computer. The aim is to situate R as part of the learning process for statistics, operational research, and data analytics. The chapter also mentions some useful resources for learning R, including books and online communities [[4]].

I hope this information helps! If you have any more specific questions or need further clarification, feel free to ask.