1. MSF 566 - Financial Time Series Analysis
Spring 2009
Syllabus
Instructor. Andrew P. Acosta
E-mail: aacosta@stuart.iit.edu
Phone: 708-267-8048
Contacting me by email is preferred, and be sure to include MSF 566 in your subject line.
Class Meeting. Tuesday. 6:00 p.m. – 8:30 p.m.
Office Hours. Personal meetings are by appointment only, however, most issues can easily be
resolved by email.
Textbook. Tsay, Ruey (2005). Analysis of financial time series (2nd ed.). Hoboken, NJ: Wiley.
(ISBN: 0-471-69074-0)
Course Description. This course develops a portfolio of techniques for the analysis of financial
time series. Distribution theory covers the normal, Student t, χ-squared and mixture of normals
models. Technical analysis covers a variety of trading rules including filters, moving averages,
channels and other systems. The first two topics are then combined into an analysis of non-linear
time series models for the mean. The course concludes with a review of volatility models including
GARCH, E-Garch and stochastic volatility models.
Some course contents will be based upon power generation and delivery, which relies heavily
upon time series analysis for modeling weather, fuel prices, electricity delivery capacity, and load.
Course Web Page. We will use the Blackboard software to share documents. You can log in
from: http://my.iit.edu.
You should,
• always check for updates to the course, including revisions to notes
• download and try out data and function code to get a better understanding of a concept
• make sure to read any announcements.

2. MSF 566 Syllabus 2
Software. Proficiency in R, S-Plus, MATLAB, Octave, and Excel is not required, but will be
used throughout the course. We will be using FinTS, the financial time series analysis R package
and textbook data sets built around Tsay (2005), and any other packages that become necessary.
R is a free software environment for statistical computing and graphics. It compiles and runs on
a wide variety of UNIX platforms, Windows and MacOS (http://www.r-project.org/).
You may obtain R, extensive documentation, and any packages to perform analysis and reporting
from the Comprehensive R Archive Network: http://cran.r-project.org/.
GNU Octave is a high-level language, primarily intended for numerical computations. It provides
a convenient command line interface for solving linear and nonlinear problems numerically, and
for performing other numerical experiments using a language that is mostly compatible with Mat-
lab. It may also be used as a batch-oriented language. (http://www.gnu.org/software/octave/).
Grading. The final grade for the course will be based on the following items,
• Mid-term exam: 40%
• Final exam: 40%
• Quizzes: 20%
Academic Integrity. This course will adhere to the university’s policy on academic honesty.
Each student is responsible for doing his or her work independently. Anyone found submitting
someone else’s work will be dealt with according to university policy. Cheating or plagiarizing will
result in failing the course.
Students with Disabilities. Reasonable accommodations will be made for students with
documented disabilities. In order to receive accommodations, students must obtain a letter of
accommodation from the Center for Disability Resources and make an appointment to speak with
Aggie Niemiec, director of the Center for Disability Resources, which is located in the Life Sciences
Building, room 218, call 312-567-5744 or email <disabilities@iit.edu>.
Course Outline
Week 1 Introduction
What is time series analysis?
Review of matrices, statistics and probability
Tsay ch. 1
Week 2 Introduction to MATLAB and R
MATLAB matrix and statistical functions
Downloading and installing R and packages
http://www.r-project.org/about.html
http://cran.r-project.org/manuals.html, especially An Introduction to R and
R Data Import/Export

3. MSF 566 Syllabus 3
Week 3 Applications of Financial Time Series
Tsay ch. 1 and readings on power generation and delivery.
Week 4 Linear Time Series Analysis
Tsay ch. 2
Week 5 ARMA, and ARIMA Models
Tsay ch. 2
Week 6 Conditional Heteroskedastic Models
Nonlinear Models
Tsay ch. 3, 4
Week 7 Time Series Data Analysis
Obtaining Data for Analysis: FREDR
, Bloomberg, BLS, etc.
Tsay ch. 4
Week 8 Midterm Exam
Week 9 High-Frequency Data Analysis
Tsay ch. 5
Week 10 Continuous-Time Models
Tsay ch. 6
Week 11 Extreme Values & Value at Risk
VaR
Expected Shortfall
Multivariate Time Series Analysis
Tsay ch. 7, 8
Week 12 Principal Component Analysis and Factor Models
Tsay ch. 9
Urga, G. (2007). Common Features in Economics and Finance: An Overview of Recent
Developments. Journal of Business & Economic Statistics, 25 (1), 2–11.
doi: 10.1198/073500106000000602
Week 13 Multivariate Volatility Models
Tsay ch. 10
Lanne, M., & Saikkonen, P. (2007). A multivariate generalized orthogonal factor GARCH
model. Journal of Business & Economic Statistics, 25 (1), 61–75.
doi: 10.1198/073500106000000404
Week 14 State-Space Models and Kalman Filters
Tsay ch. 11
Week 15 Markov Chain Monte Carlo Methods
Tsay ch. 12
Week 16 Final Exam
Document typeset using AMS-LATEX, February 24, 2009.