FinM 331 Financial Data Analysis Winter 2009

FinM 331/STAT 339 Financial Data Analysis
Winter 2009
Mondays, 6:30-9:30PM, RY 251
Syllabus⁺ and Webpage
26 March 2009 Edition

Visiting Professor Floyd B. Hanson

Instructor: Floyd B. Hanson, fhanson at uchicago dot edu.
- Office Hour: Monday, time (5PM?), MSFM Lab Eckhart Basement, but please use email anytime; weeks 3 & 4 in Singapore.
Chicago TAs/CAs/Graders:
Please Post your Homework Questions on Chalk Discussion Board to get Answers.
Office Hours: Thursdays, 4:00-5:00 pm, Chicago time, Eckhart 308.
Review Sessions: Tuesdays, 6:00-7:00 pm, Chicago time, Eckhart 203.
Virtual Review Sessions: tentatively Tuesday nights (Singapore Wednesday morning (TBA).
1. Fernando Perez Cervantes (FINM) fernandoperez at uchicago dot edu
2. Nathaniel Loman (FINM), loman at uchicago dot edu
3. Yongning Wang (FINM), ynwang at uchicago dot edu
4. Wei Yang (Frank) Wang (STAT), frankwang at uchicago dot edu
5. Ting Zhang (FINM), tingzhang at uchicago dot edu
6. Shu Zhang (FINM), shuzi at uchicago dot edu
7. Qi (Kay) Zhong (FINM), qzhong at uchicago dot edu
8. Yunda Zhong (STAT), ydzhong at uchicago dot edu
9. Singapore Teaching Assistant: Janice Quek, jquek2 at uchicago dot edu
  - Office Hour: TBA?.
  - Review Session: TBA?.
10. UBS, Stamford, CT, USA Teaching Assistant: Partha Sarkar, partha at uchicago dot edu
  - Office Hour: TBA?.
  - Review Session: TBA?.
11. This Class Webpage: Chalk ---> FINM331 ---> syllabus .
12. Text: Explanatory FINM 331 Lecture Notes;
  - L1; L2; L3; L4; L5; L6; L7; L8; L9;
  - Optionally recommended: R. Carmona, Statistical Analysis of Financial Data;
  - Optional:
    J.R. Rice, Mathematical Statistics and Data Analysis;
    S. Weisberg, Appl. Lin. Regression (see also computational version with R.D. Cook);
    R.S. Tsay, Analysis of Time Series.
13. Recommended Computational System:
  MATLAB; see Higham brothers, MATLAB Guide, SIAM Books
  (go to siam.org for 30% student discount if qualified);
  - R, S and Excel are acceptable for assignments, but you are on your own.
14. Grading:
  - Homework & Projects:
    - There will be about 8-10 graded homework sets;
      - HW1; HW2; HW3; HW4; HW5&P1; HW6; P2; HW7; HW8;
    - Homework is Assigned each week by the Lecture and is DUE in the Chalk FINM331/STAT339 Digital Dropbox before the next week's lecture, unless otherwise specified.
    - You may consult with other student about the ideas involved, but submitted homework must be the individual student's own work and similar solutions will receive discounted grades with divided credit.
    - Codes and/or worksheets need to be submitted with computational solutions.
    - Graphs should be professionally done with titles, axes labels and short description or caption.
    - Late penalties of 1 point per problem per day will be assessed for late assignments only up to one-half the earned points, but missing assigntments will receive the negative of the total points available for the assignment.
  - Exams: There will be a
    ready on or before the last class on March 9 and due by 4PM CST the Monday March 16 of exam week in Chalk's Digital Dropbox.
  - Final Grade: The grade will be based upon a combination of homework and final exam, proportioned to reflect the effort involved.

Course Outline (tentative)

Fast Introduction with Review: Probability theory, statistical analysis, real financial data and mathematical models, Limit Theorems, statistical testing, confidence intervals, prediction and risk management, computational statistics, exploratory analysis and graphical representation.
Multivariate Data Analysis,
Applied Linear Regression: OLS, parametric and non\-parametric, financial applications and computations.
Maximum Likelihood and Bayesian Parameter Estimation.
Principal Component Analysis (PCA).
Time Series Introduction.

Prerequisite Knowledge:

Introductory Probability: see for instance,
- F. B. Hanson, Online Appendix B Preliminaries in Probability and Analysis ,
- and/or J. R. Rice, Chs. (1)-2 Probability and Random Variables, Sects. 3.1-3.5 Joint Distributions, 4.1-4.4 Expected Values.
Introductory Statistics: especially linear regression:
- see review session notes, N. O. Nygaard, Introduction to Statistics and Data Analysis, ~ and perhaps the accompanying Linear Algebra Notes;
- and/or J. R. Rice, Sects. 14.1-14.5 Linear Least Squares.
Very Basic MATLAB: including Statistics Toolbox that comes with the Student Edition
MATLAB Student Version, http://www.mathworks.com/academia/student_version/.
- see also Nygaard's review session notes previously mentioned.

Some Related Resources of the Instructor:

Instructor Prior Course: Math 586 Computational Finance, Spring 2008.
Instructor Applied stochastics textbook: F.B. Hanson, Applied Stochastic Processes and Control for Jump-Diffusions: Modeling, Analysis, and Computation, SIAM Books;
- Online Appendix C: MATLAB Programs (listings of codes used to make book figures);
- MATLAB Source Codes Directory.
Quantitative Finance References and Related References .

Web Source (temp.): http://www.math.uchicago.edu/~hanson/finm331/

Email Comments or Questions to t dash 9fhans at uchicago dot edu )

FinM 331/STAT 339 Financial Data Analysis Winter 2009 Mondays, 6:30-9:30PM, RY 251 Syllabus+ and Webpage 26 March 2009 Edition

Visiting Professor Floyd B. Hanson

FinM 331/STAT 339 Financial Data Analysis
Winter 2009
Mondays, 6:30-9:30PM, RY 251
Syllabus⁺ and Webpage
26 March 2009 Edition