Fall Semester, 2026, attendance check-in
TT 2:55 - 4:15 pm in 006 Friend Center
Books | Details |
|---|---|
| Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani (2013) An introduction to statistical learning. |
| Fan, J., Li, R., Zhang, C.-H., and Zou (2020). Statistical Foundations of Data Science. CRC Press. Homepage of the book To order the book from amazon.com or from CRC Press. |
| Fan, J. and Yao, Q. (2017). The Elements of Financial Econometrics (383pp). Cambridge University Press. (for time series part) Table of Contents, a sample chapter, Figures and Computer Programs |
General Information
Instructor: Jianqing Fan, Frederick L. Moore'18 Professor of Finance. Office: 205 Sherred Hall. Phone: 258-7924. E-mail: [email protected]
Office Hours: Tuesday 9:30 am--10:30 am, Thursday 1:45 pm –- 2:45 pm (205 Sherred), or by appointment.
Precept: Arranged periodically by the TA
Teaching Assistants
- Xialu Zheng (Head TA), [email protected], 258-8787, Office: 213 Sherred Hall
Office Hours: Thursday 3-4 pm (Sherred Hall 123) and Friday 9:30 am–10:30 am (Sherred 123) - Chang Yu, [email protected]
Office Hours: Wednesday 3–4 pm (Sherred 107) and Friday 3–4 pm (Sherred 107) - Yirui Lou, [email protected]
Office Hours: Monday 3–4 pm (Sherred 107) and Wednesday 2–3 pm (Sherred 122) - Financial Econometric Lab, 222 Sherred Hall, 258-9433,
Statistics Lab, 213 Sherred Hall, 258-8787
Reference Books
- Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani (2013)
An introduction to statistical learning. Springer - Fan, J., Li, R., Zhang, C.-H., and Zou (2020).
Statistical Foundations of Data Science. CRC Press - Fan, J. and Yao, Q. (2017).
The Elements of Financial Econometrics (383pp). Cambridge University Press.
Syllabus
This course covers econometric and statistical methods as applied to finance. Topics include
- Multiple and Nonparametric Regression, Homework 1
- Generalized Linear Models
- Model Selection and Regularization
- Classification and Supervised Learning
- An Introduction to Deep Learning
- Unsupervised Learning
- Stationary time series and martingales
- Linear time series
- Discrete time volatility models
Computation
The software package for this class is R/Python. Most of the computation in this class can be done through a laptop. Laptops with wireless communication off can be used during the exams, and so can the calculators.
Attendance
Attendance of the class is required and essential. The course materials are mainly from the lecture 2:55 pm- 4:15 notes. Many conceptual issues and statistical thinking are only taught in the class. They will appear in the midterm and final exams.
Homework
Problems will be assigned through Canvas. No late homework will be accepted. Missed homework will receive a grade of zero. The homework will be graded, and each assignment carries equal weight. You are allowed to work with other students /AIs on the homework problems; however, verbatim copying of homework is absolutely forbidden. Therefore, each student must ultimately produce his or her own homework to be handed in and graded.
Exams
There will be one in-class midterm exam and a final exam. All exams are required, and there will be no make-up exams. Missed exams will receive a grade of zero. All exams are open-book and open-notes. Laptops with wireless off and calculators may be used during the exams.
Schedules and Grading Policy
| Assignment | Schedule |
|---|---|
| Homework (20%) | Various due dates (5 sets) |
| Midterm Exam (25%) | Thursday, October 15, 2026 (2:55 pm--4:15 pm, in class) |
| Final Exam (50%) | Thursday, Dec 17, 2026 (08:30 am - 11:30 am). Room TBD |
| Class participation (5%) | Class hours. |
R-labs
The following files are intended to help you become familiar with the use of R-lab commands.
Here are some useful materials too.
- An Introduction to R, by W. N. Venables, D. M. Smith and the R Core Team.
- U-Tube video: An introduction to R
- Labs 1-5: Basic skills and their associated data set (Boston housing data)
- The following extended skills are not used in the class, but are provided here for your convinience.
- Extended Skills: ANOVA and their associated data set (labor data).
- Extended Skills: GLIM and its associated data set (burn data). Description of the data set
- The following extended skills are not used in the class, but are provided here for your convinience.
- Lab 6: Linear time series analysis
- Lab 7: Discrete volatility models
- Lab 8: Capital Asset Pricing Model
Datasets used in class
Daily Data Sets
- Closing Prices of SP500: From 1/3/50 to 01/23/2019
- Closing Prices of Merck Co.: From 1/2/70 to 01/23/2019
- Dividends of Merck Co: From 6/2/70 to 12/14/18 (quarterly)
Weekly Data Sets
- Yields of 3-month Treasury Bills: From 1/8/82 to 01/18/2019
- Yields of 5-year Treasury Notes: From 1/5/62 to 01/18/2019
- Yields of 10-year Treasury Bonds: From 1/5/62 to 01/18/2019
- Ford (From May 29, 1972 to January 21, 2019)
Monthly Data Sets
- Apple (From January 1990 to January 2019)
- Ford (From January 1990 to January 2019)
- GE (From January 1990 to January 2019)
- IBM (From January 1990 to January 2019)
- Intel (From January 1990 to January 2019)
- Johnson & Johnson (From January 1990 to January 2019)
- Merck (From January 1990 to January 2019)
- Microsoft (From January 1990 to January 2019)
- S&P 500 (From January 1950 to January 2019)
- 3-month T-bill rates (From January 1934 to Feb/2011)
- 3-month T-bill rates (From January 1982 to December 2018)
- 10-year T-bond rates (From April 1953 to December 2018)
- Fama-French 3 Factors Fama-French 5 Factors Details
- 6 Portfolios Formed on Size and Book-to-Market (2 x 3) Details
- 25 Portfolios Formed on Size and Book-to-Market (5 x 5) Details
- SP500 Dividend, Dividend Yield, PE ratio, Earnings, Real Price (in Dec 2018 dollars), (Jan. 1871-Dec. 2018)
- SP500 PS and PB ratios, (quarterly, Dec. 99--Dec. 2018)
- Consumer Price Index (Jan. 1871 -- Dec. 2018)
- Personal Consumption (Jan. 1959 -- Nov. 2018)