Applied Time Series Analysis

STA2530, Fall 2024

Wednesday 10:00 am - 1:00 pm, LOCATION: Room 9016; Office Hours: Tuesday 12:00 pm - 1:00 pm
TA Session: Tuesday 11:00 am - 12:00 pm

Instructor: Xiaofei Shi xf.shi[at]utoronto(dot)ca

TAs

Yaqi Shi

Course Description: An overview of methods and problems in the analysis of time series data related to finance and insurance. The course will focus on both theory and application with real datasets using R and Python and will require writing reports. Topics include stationary processes, linear processes; elements of inference in time and frequency domains with applications; A RMA, ARIMA, SARIMA, ARCH, GARCH; filtering and smoothing time series; and state-space models.

Course Prerequisites: Statistics, (Calculus based) Probability, Linear Regression Models, Linear Algebra.

Recommended Readings:

Introductory Econometrics for Finance, Chris Brooks.
The Econometrics of Financial Markets, John Y. Campbell, Andrew W. Lo, Archie C. Mackinlay.
Time Series: Theory and Methods, Peter J. Brockwell, Richard A. Davis.
Time Series Analysis, James D. Hamilton.

Tentative Evaluation: 60% for Homework Average + 10% for Final Exam + 10% for Presentation + 20% for Report.

Homework (60%): There will be 4 homework assignments, approximately evenly spaced throughout the semester. The homework will be posted on Quercus. We will also use Quercus for submitting and grading. We highly recommend using the discussion function on Quercus for discussion. In the case of an emergency (sudden sickness, family problems, etc.), a reasonable extension will be assigned. But we emphasize that this is reserved for true emergencies.

Final Exam (10%): Details will be released in class on Nov. 20th

Project (15% report + 5% presentation): There will be a single project, and you will work in randomized teams. You will be required to understand the theory, formulate an approach to the problem, implement the numerics in matlab, R, or Python, interpret the results, and write-up a report within 9 pages (excluding references and the contribution sheet). Reports are not simply a printout of the numerical results, or bullet point answers, but should be a formalized report (written in proper academic English) containing an abstract, introduction, model setup, methodology/theory, and discussion/interpretation of results. Pay close attention to how well the results are presented both verbally and graphically as this will form part of the assessment of your work. In contribution sheet of the final report, each team member should note their contribution to the project in no more than 100 words, bullet points are acceptable, and all team members must sign the sheet.

Tentative Schedule

Date	Topic	Note
09/11	Introduction	HW1 out
09/18	Basic Concepts of Time Series
09/25	Machine Learning Methods for Time Series I	HW1 due; HW2 out
10/02	Machine Learning Methods for Time Series II
10/09	Classical Time Series Models I	HW2 due; HW3 out
10/16	Classical Time Seires Models II
10/23	Classical Time Seires Models III	HW3 due; HW4 out
10/30	Impulse Response Analysis
11/06	Forecasting and Filtering of Time Series I	HW4 due
11/13	Forecasting and Filtering of Time Series II
11/20	Class Presentation I
11/27	Class Presentation II

Academic Integrity: Academic integrity is fundamental to learning and scholarship at the University of Toronto. As a result, U of T treats cases of cheating and plagiarism very seriously. Please read the University of Toronto’s Code of Behaviour on Academic Matters available at University of Toronto website on Academic Integrity All suspected cases of academic dishonesty will be investigated following procedures outlined in the Code of Behaviour on Academic Matters. If you have questions or concerns about what constitutes appropriate academic behaviour or appropriate research and citation methods, please reach out to us. Note that you are expected to seek out additional information on academic integrity from other institutional resources, such as The Office of the Governing Council

Policy on Late Homework and/or Project Reports Late homework and/or project reports will be penalized 10% (in absolute terms) of the maximum grade for every part of a 24 hour period that it is late. For example, if you are 36 hours late and your raw grade was 86%, then you will receive a 20% penalty and your final grade for that project will be 66%.

Policy on Missing Final Exam If you miss the final exam for a legitimate reason, then you must contact the instructor within 24 hours of missing the exam (and preferably pre-emp by sending an email explaining the legitimate reason, e.g., illness). If your missed exam is deemed legitimate, then you will have the opportunity to sit through a verbal makeup exam with the instructor of no more than one hour. The content will be the same covered by the exam, but the questions asked will differ from the delivered exam.

Policy on Regrading The course regrading policy exists to correct mistakes, and any request should clearly identify the error (for example, a question that was not marked, or a total incorrectly calculated). Requests to correct such mistakes must be sent by email to the instructor and the TA, within 48 hrs after the homework/exam was returned. Note that if a student requests for a re-mark, the entire assignment may be re-marked. So, the new grade may go up, go down or remain the same.

Policy on Collaboration: You are encouraged to work together on the homework. Discussing the homework problems with one another can be a valuable learning experience. However, it is a violation of the rules on academic integrity to copy another student's solution and submit it as your own. You should write up your solutions separately, not referring to a common document. Furthermore, you should not submit any work that you do not fully understand. You should be able to start with a clean sheet of paper and without notes or assistance write out the solution to any homework solution you submit. If you will do that with every homework you submit, the similarity between your solutions and those of other students will not arouse suspicion. More importantly, you will be well prepared for the interviews. You are not permitted to use homework solutions for this course from previous years or solutions you find from other sources, including the internet. If multiple students turn in identical solutions, all of them will receive a zero. People caught cheating generally tell a story about being unable to keep up with the material. First of all, remember that you can submit homework late. Secondly, if there is something going on in your life that makes it hard for you to keep up, please let me know. That brings me to the following point.

Take Care of Yourself: It is easy for me to say and hard for all of us, including me, to do, but taking care of your physical and mental health is essential, especially during the COVID-19 pandemic. Life is a marathon, and you need to pace yourself. Do your best to maintain a healthy lifestyle by eating well, exercising, avoiding drugs and alcohol, getting enough sleep and taking some time to relax. This will help you achieve your goals and cope with stress. If you or anyone you know experiences extreme academic stress, difficult life events, or feelings of anxiety or depression, I strongly encourage you to seek support. Counseling and Psychological Services is here to help 24/7, and everything will be confidential: https://mentalhealth.utoronto.ca/
In addition, consider reaching out to a friend, faculty or family member you trust for help getting connected to the support. Keep in mind that for serious psychological issues, the first counselor you meet with may not be the right one for you, but this does not mean you should give up on counseling. Keep looking for someone who can help you.

If you or someone you know is feeling suicidal or in danger of self-harm, call immediately, day or night:
Non-urgent campus police: 416-978-2323
If the situation is life threatening, call the police:
Urgent campus police: 416-978-2222
Off campus: 911

If you have questions about this advice, your coursework, or anything else about which I might be helpful, please let me know.

Copyright Notice All course materials, including notes, examples, and questions from assessments, are the property of the author, instructors, or TA (as relevant) and may not be distributed online or by any other means. You are permitted to download session materials for your own academic use, but you should not copy, share, or use them for any other purpose without the explicit permission of the instructors.