Course: Analysis of Financial Time Series

Spring Quarter 2007

Business 41202: Analysis of Financial Time Series

Instructor: Ruey S. Tsay

Phone: 773-702-6750
Fax: 773-702-0458 (Please put my name on the cover page)
HPC: 455

Lecture:
Bus 41202-01: Fridays 8:30 am to 11:30 am, HPC 04
Bus 41202-85: Saturdays 9:00 am to 12:00 noon, GC 404

Teaching Assistant: Mr. David Matteson
e-mail: matteson@uchicago.edu
(e-mail is the easiest way to contact TA)

Review Sessions:
BS41202-01: Wednesdays 11:45 am to 12:45 pm, Room HPC04
BS41202-85: Saturdays 12:00 to 12:45 pm, Room GC 404

Syllabus of the course.

Course materials

Text: Analysis of Financial Time Series, 2nd Edition
Ruey S. Tsay, Wiley, 2005.
ISBN: 0-471-69074-0

Data sets:
http://faculty.chicagogsb.edu/ruey.tsay/teaching/fts2/
or additional datasets will be posted for lectures and homework assignments.

Lecture Notes: Will be posted here the week before lecture.
Week 1: Lecture & data set used: m-ibm2604.txt
Week 2: Lecture & data sets used: dgnp82.txt, q-unrate.txt
Week 3: Lecture & data sets used: power6.txt, jnj.txt, w-gs1n3.txt
Week 4: Lecture & data sets used: m-intc7303.txt, sp500.txt
                Long-memory estimation: armaFit
Week 5: Lecture & data sets used: m-ibmln.txt
Week 6:  Lecture & data set used: d-geohlc.txt R-source: ohlc & yang
Week 7: Lecture
Week 8: Lecture
Week 9: Lecture
Week 10: Lecture1 & Lecture2, Multivariate Ljung-Box statistics: mq
                  Data sets used: d-iyr.txt, d-vnq.txt

Computing:
The main package used is R, which is free from R-Project for Statistical Computing.
The following packages are needed in R:
(fBasics, fSeries, tseries, nnet, evir)
It also needs Ox with G@RCH to fit various GARCH models.

[Students may use other packages or programs if they prefer. ]

R and Ox Installation:

Instructions for download R and G@RCH: G@RCH-info (readme)
   1. Download R here.
   2. Download Ox here.
   3. Download G@RCH here.
   4. Modified GarchOxModelling file
5. A corrected version of the garchOxFit function.
(This part is from Prof. K.S. Chan of University of Iowa)
   6. A (dated) tutorial of G@RCH. (document)

Additional information of R with Rmetrics:
Web site for Rmetrics: www.itp.phys.ethz.ch/econophysics/R/
(including some documents and list of commands)

Instructions for running R on PC. R-start
Demonstration: in class

Homework :
Assignment before class: Read Chapter 1 of the textbook.
HW assignment 1: HW1 due on April 6 & 7, respectively.
Data sets: d-cmesp0406.txt, m-aigvwewsp8006.txt, w-tb3ms07.txt,
d-exjpus.txt, d-exuseu.txt
Solution: HW1s

HW assignment 2: HW2 due on April 13 & 14, respectively.
Data sets: m-dec19.txt, m-cpileng.txt, q-gnprate.txt
Solution: HW2s R-output used: hw2s-out

HW Assignment 3: HW3 due on April 20 & 21, respectively.
Data sets: (1) Mortgage, (2) Treasury, (3)&(4) Decile-1, (5) AA-earn
Solution: HW3s, R-output used: hw3s-out

HW assignment 4: HW4 due on May 4 & 5, respectively.
Data sets: (1)-(3) d-sbuxsp0106.txt, (4) m-pg5606.txt, (5) d-exuseu.txt
Solution: HW4s, R-output used: hw4s-out

HW assignment 5: HW5 due on May 18 & 19, respectively.
Data sets: (1)-(2) d-catohlc0107.txt, (3)-(5) m-ge2606.txt
Solution: HW5s Output used: hw5s-07-otp.txt

HW assignment 6: HW6 due on June 1 & 2, respectively.
Data sets: (3)-(5) d-aapl9606.txt, d-aig9606.txt
Solution: HW6s Output used: hw6s-07-otp.txt

Office hour : (a) Wednesdays 1:30 pm to 2:30 pm
(b) By appointment
(c) E-mail me at any time with questions.
(This is the easiest way to reach me.)

Midterm : Week 6. Open book and notes!
                Date: Campus session: May 4
                          Weekend session: May 5
                Lecture to follow after the exam.


Final Exam: (New) at Exam week
               Campus:   Friday, June 8, 8:00 am to 11:00 am
               Weekend:  Saturday, June 9, 9:00 am to 12:00 noon.
               Open book and notes.

Sample Final Exam: Sample & Solutions

Grading: 35% midterm + 35% final exam + 30% homework,
where the scores of midterm, final exam and homework
assignments are normalized to be out of 100.

The GSB mandates a maximum class grade point average of 3.33.
I rank the class based on the final scores using the above grading
formula and pick grade cutoffs so that I can get the highest class
GPA under the constraint.

Additional Web Sites for data:
(a) Wharton WRDS at http://wrdsx.wharton.upenn.edu
(b) St Louis Fed at http://research.stlouisfed.org/fred2/

Feedback:

Mid-term: Exam, including R output
Solution:

Final Exam: Exam & Solution

Old mid-term tests:
Year 2005: midterm, output, and solutions
           R output for 2005 Exam
Year 2006: mideterm, ouput, and solutions