Spring Quarter 2016

Business 41202: Analysis of Financial Time Series

Instructor: Ruey S. Tsay

ruey.tsay@Chicagobooth.edu

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

Lecture:
Bus 41202-01: Friday AM to 11:30 AM at Class Room 10, Harper Center
Bus 41202-85: Saturday 1:30 PM to 4:30 PM at NBC 130, NBC Tower

Teaching Assistant:  Mr. Jae Hyen Chung
e-mail: jchung4@chicagobooth.edu
(e-mail is the easiest way to contact Jae)

Review Sessions
BS41202-01: Thursdays 12:15 PM to 1:15 PM (Room HCC09). (Starts from Week 2.)
BS41202-85: Saturdays 12:15 PM to 1:15 PM (Booth 455-130).
(Starts from Week 1.)

Syllabus of the course.

Course materials

Text: Analysis of Financial Time Series, 3rd Edition
          Ruey S. Tsay, Wiley, 2010.
          ISBN: 0-470-41435-4

Data sets:
http://faculty.chicagogsb.edu/ruey.tsay/teaching/fts3/
or additional datasets will be posted for lectures and homework assignments.

Lecture Notes:  posted a week before lecture. (Data sets will be updated)
>> Print a copy of the lecture notes before class. <<

Pre-class reading: Note and data sets: d-ibm-0110.txt, d-vix0411.csv
Week 1: Lecture & Data sets used: d-aapl0413.txt, d-exuseu.txt, m-ibm-6815.txt, m-tb3ms.txt
m-tb6ms.txt, q-ko-earns8309.txt, taq-cat-t-jan042010.txt, d-cdsJPM.txt

Week 2: Lecture & Data sets used: dgnp82.txt
, d-ibmvwew6202.txt, m-dec12910-6114.txt
R script: lagplot.R

Week 3: Lecture & Data sets used: q-gdpc96.txt, w-gs1n36299.txt, m-hstarts-5912.txt, q-earn-jnj.txt, m-tb3n6.txt,
UNRATE-1.txt

Week 4: Lecture & Data sets used: VIX and SP5 index downloaded via the quantmod package, m-intc7303.txt, sp500.txt
(start to use the fGarch package).

Week 5: Lecture & Data set used: m-ibm2609.txt
(Addtional packages used: Introduction) rugarch and stochvol. The introduction gives examples.

Week 7: Lecture & Data set used: download directly using quantmod.

Week 7a: Lecture & Data sets used: m-gmsp6708.txt, m-ibmln2699.txt, taq-jnj-t-oct4t152010.txt, ibm91-ads.txt, ibm91-adsx.txt,
taq-cat-t-jan042010.txt, taq-cat-cpch-jan042010.txt

Week 8:Lecture, Data sets used:

Week 9: Lecture & portfolio, Data set: d-ibm-rq.txt, d-ibmbaml3a-0110.txt, d-ibm-0110.txt
R packages used: evir, quantreg, and fGarch.

Week 10: Lecture & Data sets used:q-gdpun.txt, d-bhp0206.txt, d-vale0206.txt, d-ibmbaml3a-0110.txt;
R packages used: urca and MTS

Computing:
The main software used is R, which is free from R-Project for Statistical Computing.
The most recent version of R is R3.1.2
The following packages are needed in R:
(fBasics, fGarch, quantmod, fUnitRoots, MTS, nnet, evir, stochvol, urgarch, urca)
[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() ]

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

R Installation:

Instructions for download R:
   1. Download R here. [Click CRAN to select a mirror site; a web site close to you.]

Instructions for running R on PC will be demonstrated in class.

[* Students may also use RStudio (which can also be found free on the web). *]

R commands used in lectures: (updated weekly)
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 6:
Week 7: Rcommands_lec7.txt
Week 8: Rcommands_lec8.txt
Week 9: Rcommands_lec9.txt
Week 10: Rcommands_lec10.txt

Homework :
Assignment before class: Read Chapter 1 of the textbook.
HW1: Data sets used: d-sbux3dx-0715.txt, m-aapl3dx-0115.txt, d-exuseu-0516.txt
Solutions & R-commands used. The R output is for your reference only.
HW2 : Data sets used: m-globaltemp.txt, m-COILWTICO.txt
Solutions & R-commands used.
HW3 : Data sets used: m-dec12910-6114.txt, bond6m-6115.txt, d-cdsJPM.txt
Solutions & R-commands used.
HW4 : Data sets used: m-ibmsp-6115.txt
Solutions & R-commands used.
HW5 : Data sets used: m-pgsp-4115.txt, taq-t-sbuxdec2031-2014.txt (Due May 27 and 28, respectivley).
Solutions & R-commands used.
HW6: Date sets used: taq-sbux-pch-dec22-2014.txt, (also use quantmod to download the stock data.)
Solutions & R-commands used.

 

Office hour : (a) Thursday: 4:00 PM to 5:00 PM (Harper Center Rm 455)
                      (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 6 (Friday)
                          Weekend session: May 7 (Saturday)

             
              
Final Exam: Exam week
    Campus:   Friday, June 10, 8:30 AM to 11:30 AM
     Weekend: Saturday, June 11, 12:30 PM to 3:30 PM (Graduating); 1:30 PM to 4:30 PM (other students)
     Open book and notes.


Grading: 30% midterm + 35% final exam + 5% in-class discussion + 30% homework,
where the scores of each component are normalized to be out of 100.

Chicagobooth 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.

Provisional Grade: For graduating students, provisional grades will be assigned based on the midterm and hw assignments
that are available via Week 8.

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:
0. Moving average plot: ma.R
1. Forecasts with specified forecast origin: fore.R & forecast plot: foreplot.R ; one-step predictions: foreOne.R
2. Recursive out-of-sample forecasts: backtest.R
3. To estimate an IGARCH model: Igarch.R & GARCH-M program: garchM.R
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. Compute volatility of a given GARCH11 model: garch11v.R
(including IGARCH(1,1) model for RiskMetrics).
8. Co-integration test:
(Use urca package)
9. ACD estimation: acd.R
10. High-frequency intraday log returns: hfrtn.R
11. High-frequency intraday number of transactions: hfntra.R
12. High-frequency price change and duration: hfchg.R
13. Compute VaR based on traditional EVT estimates: evtVaR.R
14. Risk measure: RMeasure.R
15. TGARCH11 & exp-GARCH models: Tgarch11.R & Egarch.R
16. R program for SV model: svfit.R (no leverage effect); See also the R package "stochvol"
17. R program for RiskMetrics: RMfit.R

Feedback: file

Mid-term: Exam and Solutions
               

Final Exam: Exam and Solutions

Old exams :     

Year 2014: midterm and solutions  
          Final exam and solutions

Year 2015: midterm and solutions  
          Final exam and solutions