S8602027X: Computational Finance

Instructor:James Gentle
email: jgentle@gmu.edu

Lectures: Tuesdays and Thursdays 13:30 - 16:55, E206

Time series analysis is one of the most important statistical methods in finance and economics. This course will cover the basic ARMA models; conditional heteroscedastic models, with an emphasis on GARCH models; nonlinear models such as threshold models; and, as time permits, continuous time models.

The emphasis will be on the mathematical models, the statistical methods, and the computations, rather than on topics in the domain of finance.


Some knowledge of statistical theory and methods is a prerequisite. Knowledge of advanced calculus and differential equations is also required. Some general background in finance will be useful.

Main text

The text is Analysis of Financial Time Series, third edition, by Ruey S. Tsay (2010). ISBN 978-0-470-41435-4
The general flow of the course follows this text. We will cover most of the first 5 chapters, plus assorted material from other chapters, and supplementary material.
Other References


The main software used will be R, which can be obtained without cost from the R Project for Statistical Computing.

Students who are familiar with SAS, Matlab, or other statistical packages are encouraged to explore the use of these packages for analysis of financial time series. These other packages may be used for the assignments if the packages provide the required functionality, but examples discussed in class will use R.


Data, of course, are the raw materials of any statistical analyses. There is a wealth of easily accessible financial data. For this course we will generally be interested in daily, weekly, or monthly closing prices and the volume corresponding to the periods. An easy source of the kind of data we will use is Yahoo. Also, we will use data that the author of the text has collected into files at his website. Another important data source is the US Federal Reserve Economic Data (FRED) provided by the Federal Reserve Bank of St. Louis.

Approximate schedule

May 19

May 21

May 26

May 28

June 2

June 4

June 9

June 11

June 16

June 17

15:00-17:00 Two-hour exam on material covered through June 16.
Closed book and notes, except for one sheet (front and back) of prewritten notes.
Approximately half of the exam will involve programming in R.
  • Solutions.

    June 18