Spring Quarter 2009

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 at C02, Harper Center

Bus 41202-85: Saturdays 9:00 am to 12:00 pm at Room 200, Gleacher Center

Teaching Assistant: Mr. Paco Vazquez-Grande

e-mail: fvazque1@chicagobooth.edu

(e-mail is the easiest way to contact TA)

Review Sessions:

BS41202-01: Thursdays 11:45 am to 12:45 pm at C02, Harper Center

BS41202-85: Saturdays 12:00 pm to 12:45 pm at Room 200, Gleacher Center

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 sets used: m-ibm6708.txt Norwegianfire.txt , d-aaplsp0007.txt , d-jpus.txt

Week 2: Lecture & Data sets used: dgnp82.txt, m-unrate.txt,

Week 3: Lecture & Data sets used: q-earn-jnj.txt, q-earn-fdx.txt, power6.txt, w-gs1n36299.txt, q-gdp05.txt

Week 4: Lecture & Data sets used: m-intc7303.txt, d-sp55008.txt, vix0409.txt

Demonstration of volatility modeling using the fGarch package: fGarch.txt

Week 5: Lecture & Data sets used: sp500.txt , m-ibm2697.txt

Week 6: Lecture, Data set used: d-sp55008.txt & Midterm

Week 7: Lecture & Data sets used: m-gmsp6708.txt, d-ibm3dx6203.txt, m-ibmln2699.txt

Week 8: Lecture & data sets used: ibm91-ads.txt, ibm91-adsx.txt, d-aapl9907.txt

Week 9: Lecture & data set used: d-ibmln98.dat

Week 10: Lecture & data sets used: q-gdpun.txt, d-bhp0208r.txt, d-rio0208r.txt

Computing:

The main package used is R, which is free
from R-Project for Statistical Computing.

The most recent version of R is R2.8.1

The following
packages are needed in R:

(FinTs, fBasics,
fSeries, fGarch, fUtilities, fUnitRoots, tseries,
nnet, evir)

[In fact, you may want to install the complete package Rmetrics. This can be done in R using

the following two commands:

source("http://www.rmetrics.org/Rmetrics.R")

install.Rmetrics()
]

In Particular, Dr. Spencer Graves has created a R Companion to the textbook.

The package is called FinTS and can be download from CRAN of R similar to other packages. This is an ongoing project, but it has all data files used in the text and script files to perform most of the analyses in the first few chapters. You can also download the associated document from CRAN.

The class 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. [Click CRAN to select a mirror site.]

==> Students should install R first.

2. Download Ox here. [This part of the program will be used later for some volatility modeling.]

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)

Instructions
for running R on PC. R-start

Demonstration: in class

R commands used in lectures:

Week 1: Rcommands_lec1.txt

Week 2: Rcommands_lec2.txt

Week 3: Rcommands_lec3.txt

Week 4: Rcommands_lec4.txt

Week 5: Rcommands_lec5.txt

Week 7: Rcommands_lec7.txt

Week 8: Rcommands_lec8.txt

Week 9: Rcommends_lec9.txt

Week 10: Rcommands_lec10.txt

Homework :

Assignment before class: Read Chapter 1 of the textbook.

**HW1**: Data sets used: d-csp0108.txt, m-gmsp6708.txt, m-tb3ms.txt

Solutions

**HW2**: Data sets used: m-deciles08.txt, d-bacohl0108.txt, m-unrate.txt

Solutions & R output used.

**HW3**: Data sets used: m-mortg7109.txt, m-tb3ms7109.txt, m-deciles08.txt

Solutions & R output used.

HW4: Data sets used: d-dellvw0008.txt, m-3msp4608.txt, d-useu9909.txt

Solutions & R output used.

HW5: Data sets used: d-intc0209.txt, m-unrate.txt, d-useu9909.txt

Solutions & R output used.

HW6: Data sets used: d-aapl9908.txt, d-wmt9908.txt

Solutions & R output used.

Office hour
: (a) Thursdays 10:30 am to 11:30 am

(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 8

Weekend session: May 9

Lecture to follow after the exam.

Final Exam:
(New) at Exam week

Campus: Friday, June 12, 8:00 am to 11:00 am

Weekend: Saturday, June 13, 9:00 am to 12:00 pm.

Open book and notes.

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://wrds.wharton.upenn.edu

(b) St Louis Fed at http://research.stlouisfed.org/fred2/

Additional R scripts:

1. Forecasts with specified forecast origin: fore.R & forecast plot: foreplot.R

2. Recursive out-of-sample forecasts: backtest.R

3. To estimate EGARCH models, use the following two modified files:

(a) GarchOxModelling_w file and (b) garchoxfit_w file [stored in the same directories

as the original ones.]

4. Moving window for volatility calculation: mvwindow.R

5. Yang and Zhang's methof: yz.R

6. Recursive out-of-sample nnet forecasts: backnnet.R

7. Calculate VaR based on EVT: evtVaR.R

8. Compute volatility of a given GARCH11 model: garch11v.R

(including IGARCH(1,1) model for RiskMetrics).

9. Multivariate Ljung-Box statistics: mq.R

10. Co-integration test: coint.R

11. ACD estimation: acd.R

Feedback: fback

Mid-term: Midterm and soultions

Old exams :

Year 2007: midterm, output,
and solutions

Final exam, output, and solutions

Year 2008: midterm, and solutions

Final exam, and solutions