STAD70 Statistics & Finance II (Winter 2022)
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STAD70 Statistics & Finance II (Winter 2022)
Syllabus
Instructor: Leonard Wong (email: [email protected])
Lectures: Monday 2pm–4pm (HW 214), Wednesday 4pm–5pm (IC 326).
The initial lectures (at least until January 31) will be conducted on Zoom at:
https://utoronto.zoom.us/j/7522990199
Announcements and course materials will be posted on Quercus.
Office hours: Wednesday 3–4pm and 5–6pm or by appointment
Grader: Peng Liu (email: [email protected])
Online forum: You are encouraged to use Piazza to (anonymously) ask questions about the course and discuss among yourselves. You can join using the following link:
piazza.com/utoronto.ca/winter2022/stad70
1 Outline
This course is an introduction to some applications of statistical and computational tools in quantitative finance. Specifically, we focus on two important and closely related topics:
(i) Financial econometrics, i.e., statistical modeling of financial data and tests of finan- cial/economic hypotheses.
(ii) Quantitative investment, i.e., the construction, backtesting, and implementation of invest-
ment strategies using quantitative methods.
UTSC course website: https://utsc.calendar.utoronto.ca/course/STAD70H3
Textbook: We will use our own lecture notes whose materials are mainly taken from the following textbooks:
❼ Statistics and Data Analysis for Financial Engineering (2nd edition) by Ruppert and Mat-
teson (2019). It is available at https://link.springer.com/book/10.1007/978-1-4939-2614-5
❼ Analysis of Financial Time Series (3rd edition) by Tsay (2010). It is available at https://
onlinelibrary-wiley-com.myaccess.library.utoronto.ca/doi/book/10.1002/9780470644560
❼ Financial Econometrics: Models and Methods by Linton (2019).
2 Grading scheme
❼ 30%: Assignments
❼ 30%: Midterm (2 hours) (around week 7, date to be confirmed)
❼ 40%: Final exam (3 hours)
Assignments (30%)
There will be 4 assignments. The assignments are to be submitted online through Quercus. Your work may be handwritten (professionally scanned) or typed (preferably using LATEX). Some assignment problems require R programming. For these problems, you need to submit
both the source codes and the output.
Late submission policy:
❼ 1 minute to 23 hours and 59 minutes: 20% penalty.
❼ 24 hours to 1 day 23 hours 59 minutes: 40% penalty, and so on.
Midterm (30%)
The date and time will be announced later. If you are not able to attend the midterm for a valid reason (e.g. medical), you must let me know as soon as possible. Then you will need to take a make-up test (which may be conducted as an oral exam).
Final exam (40%)
The date and time will be announced later. It will cover all materials covered in the course (before and after the midterm).
3 Tentative schedule
Week 
Topic
Introduction
Asset returns
Stylized facts of asset returns
Tools from time series analysis
Efficient market hypothesis
Volatility modelling
Risk management
Mean-variance portfolio selection
Capital asset pricing model
Factor models
Statistical arbitrage
Growth optimal portfolio
4 Important information
Accessibility
Students with diverse learning styles and needs are welcome in this course. In particular, if you have a disability/health consideration that may require accommodations, please feel free to approach the instructor and/or the UTSC AccessAbility Service as soon as possible. Enquiries are confidential. The UTSC AccessAbility Services staff are available by appoint- ment to assess specific needs, provide referrals and arrange appropriate accommodations, at [email protected].
Religious accommodations
The University has a commitment concerning accommodation for religious observances. I will make every reasonable effort to avoid scheduling tests, examinations, or other compulsory activ- ities on religious holy days not captured by statutory holidays. According to University Policy, if you anticipate being absent from class or missing a major course activity (like a test, or in-class assignment) due to a religious observance, please let me know as early in the course as possible, and with sufficient notice (at least two to three weeks), so that we can work together to make alternate arrangements.
Academic integrity
The University treats cases of cheating and plagiarism very seriously. The University of Toronto’s Code of Behaviour on Academic Matters (https://governingcouncil.utoronto.ca/secretariat/ policies/code-behaviour-academic-matters-july-1-2019) outlines the behaviours that con- stitute academic dishonesty and the processes for addressing academic offences. Potential of- fences in papers and assignments include using someone else’s ideas or words without appropriate acknowledgement, submitting your own work in more than one course without the permission of the instructor, making up sources or facts, obtaining or providing unauthorized assistance on any assignment. On tests and exams cheating includes using or possessing unauthorized aids, looking at someone else’s answers during an exam or test, misrepresenting your identity, or falsifying or altering any documentation required by the University, including (but not limited to) doctor’s notes.
Course materials, including lecture notes
Course materials are provided for the exclusive use of enrolled students. Do not share them with others. I do not want to discover that a student has put any of my materials into the public domain, has sold my materials, or has given my materials to a person or company that is using them to earn money. The University will support me in asserting and pursuing my rights, and my copyrights, in such matters.
2022-01-29