Chapter 1 Introduction to Financial Analytics

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:

  1. Paul Teetor, The R Cookbook
  2. Phil Spector, Data Manipulation with R
  3. Norman Matloff, The Art of R Programming: A Tour of Statistical Software Design
  4. John Taveras, R for Excel Users at

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.

  1. The standard start-up is at CRAN A script in the appendix can be dropped into a workspace and played with easily.

  2. Julian Faraway’s is a fairly complete course on regression where you can imbibe deeply of the many ways to use R in statistics.

  3. Along econometrics lines is Grant Farnsworth’s

  4. Winston Chang’s and Hadley Wickham’s example at are online graphics resources.

  5. Stack Overflow is a programming user community with an R thread at The odds are that if you have a problem, error, or question, it has already been asked, and answered, on this site.

  6. For using R Markdown there is a short reference at Cosma Shalizi has a much more extensive manual at

1.3.3 Install R on your computer

Directions exist at the R website, for installing R. There are several twotorials, including some on installation that can be helpful at

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

  • 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 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.

  1. Click on and navigate down to the Download Desktop button and click.

  2. Click on the Download button for the RStudio Desktop Personal License choice.

  3. 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 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.

  1. 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.

  1. 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).

  1. 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

  • 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,, 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.

  1. Install the MikTeX document rendering system for Windows or MacTeX document rendering system for Mac OS X.
  • For Windows, navigate to the 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 COMPLETE installation. Frequently Asked Questions (FAQ) can be found at If you have RStudio already running, you will have to restart your session.

  • For MAC OS X, navigate to the 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 FULL installation. Frequently Asked Questions (FAQ) can be found at If you have RStudio already running, you will have to restart your session. FAQ can be found at

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.


The Rmd file contains three types of content:

  1. 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
  1. Chunks of R code surrounded by ``` (find this key usually with the ~ symbol).
  2. 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__
superscript^2^ and subscript~2~


# 1st Level Header

## 2nd Level Header

### 3rd Level Header


*   Bulleted list item 1

*   Item 2

    * Item 2a

    * Item 2b

1.  Numbered list item 1

1.  Item 2. The numbers are incremented automatically in the output.

Links and images


[linked phrase](

![optional caption text](path/to/img.png)


First Header  | Second Header
------------- | -------------
Content Cell  | Content Cell
Content Cell  | Content Cell




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

1.3.8 jaRgon

(directly copied from Patrick Burns at, 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.