Time Series Analysis for Business and Finance

MSAN 604
Time Series Analysis for Business and Finance
Instructor: Jeff Hamrick, Ph.D., CFA, FRM
Course Syllabus
Fall 2013
SUMMARY INFORMATION
Instructor: Jeff Hamrick, Ph.D., CFA, FRM
Office: Masonic 211
Office Hours: By appointment
Cell Phone: 617/943-4619
Office Phone: 415/422-6810
Email Address: jhamrick@usfca.edu
Class Location: 101 Howard room 454
Class Time: 10:00 a.m. - 12:30 p.m., Tuesdays and Thursdays


ON COURSE GOALS. Any student who successfully completes this course should:

  • Understand the concepts of weak stationarity, strong stationarity, and white noise;
  • Determine the properties of moving average and autoregressive models, as well as autoregressive moving average models, using elementary algebra and by taking both expectations and conditional expectations;
  • Be able to use, and interpret, the sample autocorrelation function and sample partial auto correlation function;
  • Be able to use R to conduct parameter estimation, model selection, and diagnostic testing related to the class of Box-Jenkins models;
  • Be able to model seasonal data (e.g., quarterly revenues, business inventories, etc.) using SARIMA models;
  • Understand and deploy various smoothing techniques (e.g., moving averages, single exponential smoothing, double exponential smoothing, and Holt-Winters smoothing) on time series data;
  • Understand properties of ARCH, GARCH, MGARCH, and EGARCH models, and how to undertake parameter estimation and model selection with respect to these classes of models, using R;
  • Be familiar with unit root tests and discrete random walks;
  • Forecast the level of a time series (in the case of the Box-Jenkins class of models) or the volatility of the driving white noise (in the case of ARCH and GARCH models);
  • Understand the concept of value-at-risk (VaR), and be able to use ARCH and GARCH models to support the forecasting of value-at-risk; and
  • Be able to take time series data within some business context, choose and t an appropriate time series model, validate the fitted residuals, and then create forecasts that are useful within that business context.

ABOUT ME. My name is Jeff Hamrick. I'm a term assistant professor of nance and business

analytics and I am affiliated with both the Master of Science in Business Analytics (MSAN) and

Master of Science in Financial Analysis (MSFA) programs at the University of San Francisco.


Please call me Jeff. My office is located in room 211 of the Masonic (MA) building at the cor-

ner of Masonic and Turk. My e.mail address is jhamrick@usfca.edu. My cell phone number is

617/943-4619 and my office number is 415/422-6810. If you're unable to discuss academic issues

with me at the Presidio campus before or after class, let me know and we may be able to schedule

an appointment (possibly over Google Hangout) at an alternate time.


ABOUT YOU. You should be hard-working and enthusiastic about learning and, in most cases,

you are a candidate for the Master of Science in Analytics at the University of San Francisco.

Linear Regression Analysis (MSAN 601) is a prerequisite for this course.


ABOUT US. We will meet to talk about time series modeling from Tuesday, October 16, 2012 to

Tuesday, December 11, 2012. We will meet at the Presidio Campus. We will primarily use the fifth

and eighth chapters of the second edition of Introductory Econometrics for Finance by Chris Brooks

(ISBN 978-0521694681) and the first three chapters of the third edition of Analysis of Financial

Time Series by Ruey Tsay (ISBN 978-0470414354). You are responsible for the material in

all readings assigned for this course, regardless of whether or not the material from

those readings is included in my in-class lectures.


ON R. R is a powerful open-source programming language and software environment for statistical

computing and graphics. The R language is used by many professional statisticians and is making

deep inroads in industry as well. R is equipped with a wide variety of statistical and graphical

techniques. It supports linear and nonlinear modeling, classical statistical tests, time series anal-

ysis, classication analysis, clustering, and much more. It will be extensively used in the MSAN

program. A set of screencast tutorials related to R will be available on my YouTube channel and

Blackboard.


ON ATTENDANCE. This course meets for only seven weeks. It will be short and intense.

Consequently, you may only miss class under the most dire of circumstances. These circumstances

should be both unusual and documentable. For example, having a bad cold is documentable but

not unusual. On the other hand, being kidnapped by aliens is unusual but is most likely not doc-

umentable. A single absence, for any reason, is acceptable and will not be penalized.

Each absence in excess of one absence will cause your final letter grade in this class

to be lowered by one level (e.g., an A- will become a B+.)


ON LAPTOPS. In general, I want you to have a laptop in class and I want you to install R on

that laptop before the course begins. You will be expected to use R on quizzes and on the final

examination, and sometimes we will use R in class. I would ask you to be respectful of your class-

mates and to refrain from surfing the web, checking out Facebook, tweeting people your various

tweets, etc. during the middle of my lectures.


ON HOMEWORK. Every week, there will be a collection of homework problems (generally

including regressions or time series analyses for you to run using R) that I will make up myself or

assign from the Brooks or Tsay textbooks. You must work on these problem sets in groups of size

two to four and turn in a single assignment. While I encourage you to work with your colleagues

on the assigned problems as you prepare a common write-up, make sure that you are learning the

material individually rather than passively learning while somebody else does the work. Each day,

your group (which can vary from assignment to assignment) will turn in the entire collection of

problems and I will grade a random subset of them, or all of them. To facilitate efficient grading,

your weekly homework should have the following properties:


  1.  Each problem should be started on a separate piece of paper.
  2.  Different parts of the same problem do not need to be started on separate pieces of paper.
  3. Turn in the problems in the same order in which they were assigned.
  4. Staple your homework assignment in the upper left-hand corner.
  5. In general, do not print out entire data sets.
  6. In general, do not print out reams and reams of R outputs. Everything should be orderly and easy for me to read.


Unfortunately, we won't have enough time to do homework problems in class or to discuss home-

work problems in great detail. Instead, feel free to come to my virtual office hours or to schedule

an individual appointment with me. I will not accept late homework assignments under

any circumstances.


ON QUIZZES. We will take a quick quiz at the beginning of class every Tuesday (though not

the first week of class). Each in-class quiz will focus on material that we have recently discussed in

class (generally, the topics from the prior day). The in-class quizzes will be centered on denitions,

concepts, and simple computations, as well as interpretation of pre-generated statistical output.

At the end of the course, I will drop your lowest quiz grade.


ON THE FINAL EXAMINATION. There will be a final written comprehensive examination

in this course on December 11 during the regular course time, with some possibility for extra time

(say, 10:00 a.m. - 2:00 p.m.). The final examination will focus on concepts, i.e., you will not be ex-

pected to engage in tons of routine calculations, but you will be expected to know certain formulas

and relationships and you will be expected to interpret the outputs of various time series analyses.

In addition, you will be expected to use R to assist you with time series analysis.


ON GRADING. I've noticed that students are often too focused on grades, to the great detri-

ment of their own learning. If students put as much effort into actually learning material as they

did worrying about their grades, their performance would be much better. Nevertheless, part of

my job is to assign grades fairly and in a manner that reflects the high academic standards at the

University of San Francisco and in the MSAN program. In this class, we will use the standard

ten-point scale. "Plus" or "minus" grades will be assigned to students with grades close to the

extremes of each ten-point bracket (plus or minus three points from the boundary of each bracket).

Your grade in this course will be computed according to the following weights:


Component                  Weight

Homework Sets             25%

Quizzes                          25%

Final Project                   25%

Final Examination          25%


ON CHEATING. As a Jesuit institution committed to cura personalis -- the care and education

of the whole person -- the University of San Francisco has an obligation to embody and foster the

values of honesty and integrity. The university upholds standards of honesty and integrity from all

members of the academic community, including faculty, students, and staff. All students are ex-

pected to know and to adhere to the university's honor code. You can find the full text of the code

online at http://www.usfca.edu/catalog/policies/honor/. Specifically, while you are required

to work in groups with students on the homework assignments, you should not allow your name

to be placed on a group write-up if it does not reect your own understanding of the material and

if you have not made an honest, equitable contribution to the group effort. Copying answers from

other students or sources during a quiz or examination is a violation of the university's honor code

and will be treated as such. You are also, of course, bound to the terms of the MSAN Code of

Conduct that you signed prior to matriculating in the analytics program. All incidents of cheating

or other academic misconduct will be reported to the director of the MSAN program.


ON DISABILITIES. If you are a student with a disability or disabling condition, or if you think

you may have a disability, please contact USF Student Disability Services (SDS) at 415/422-2613

within the first week of class, or immediately upon onset of the disability, to speak with a disability

specialist. If you are determined eligible for reasonable accommodations, please meet with your

disability specialist so they can arrange to have your accommodation letter sent to me, and we will

discuss your needs for this course. For more information, please visit http://www.usfca.edu/sds/

or call 415/422-2613.