ECO4185 FINANCIAL ECONOMETRICS Summer Term 2022
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FINANCIAL ECONOMETRICS
ECO4185[A/AV]
Summer Term 2022
Official Course Description
Introduction to econometric methods used to analyze financial data. Topics covered may include modelling of stock prices and asset returns, tests of market efficiency, modelling volatility, event study analysis, and analysis of high-frequency data.
Prerequisite: ECO 2151 (ECO3151) or MAT 3375.
Indigenous Affirmation
ANISHINÀBE
Ni manàdjiyànànig Màmìwininì Anishinàbeg, ogog kà nàgadawàbandadjig iyo akì eko weshkad. Ako nongom ega wìkàd kì mìgiwewàdj.
Ni manàdjiyànànig kakina Anishinàbeg ondaje kaye ogog kakina eniyagizidjig enigokamigàg Kanadàng eji ondàpinangig endàwàdjin Odàwàng.
Ninisidawinawànànig kenawendamòdjig kije kikenindamàwin; weshkinìgidjig kaye kejeyàdizidjig. Nigijeweninmànànig ogog kà nìgànì sòngideyedjig; weshkad, nongom; kaye àyànikàdj.
ENGLISH
We pay respect to the Algonquin people, who are the traditional guardians of this land. We acknowledge their longstanding relationship with this territory, which remains unceded.
We pay respect to all Indigenous people in this region, from all nations across Canada, who call Ottawa home. We acknowledge the traditional knowledge keepers, both young and old.
And we honour their courageous leaders: past, present, and future.
Inclusion
The University of Ottawa aims to be an equitable and inclusive institution, actively participating in ensuring the wellbeing of students, personnel and faculty members. The University is committed to eliminating obstacles to student inclusion in accordance with theOntario Human Rights Code. The Code provides that every person has the right to equal treatment with respect to goods, services, facilities, housing, contracts and employment as well as membership in trade or professional associations and unions without discrimination because of ‘’Race, Ancestry, Place of origin, Colour, Ethnic origin, Citizenship, Creed, Sex, Sexual orientation, Gender identity, Gender expression, Age, Record of offence (in employment only), Receipt of public assistance, Marital status, Family status, Disability’’ .
TheHuman Rights Office of the University of Ottawaadds ‘’although the Human Rights Code does not provide a definition of discrimination, the notion of discrimination covers unfair treatment on the basis of race, disability, sex, or any other personal characteristic. It can take many different forms, can target a single person or a group and can be part of a system.’’
If you have experienced discrimination or harassment, you can seek confidential assistance through the University Human Rights Office to discuss your situation and/orto file a formal complaint.
The following uOttawa Campus based services are available to you and your fellow students:
uOttawa Counselling Serviceincluding Individual Counselling provided by uOttawa Counsellor, Pierre Bercy who specializes in anti-black racism;
University of Ottawa Students’ Union (UOSU)Resources for/from the Black Community,Centre for Students with Disabilities,Racialized and Indigenous Students Experience Centre,Womxns Resource CentreanduOttawa Pride Center
Anti-racism student committee (Email:car.arc.uottawa@gmail.com)
Mashkawazìwogamig:Indigenous Resource Center
University of Ottawa’s Human Rights Officeincludingpolicies on accessibility.
Course Learning Outcomes
The objective of this course is to introduce students to the methods used for the analysis of time series and financial data. Students will first develop an understanding of univariate and multi-variate time series models, their identification and estimation, and the techniques used for model evaluation. They will then learn how to apply time series models for forecasting purposes. Finally, students will become familiar with the methods used for modeling volatility and correlation in the data, and with their use in macroeconomics and financial economics.
By the end of the course, students will also be able to implement the concepts learned in class to address empirical questions using statistical software and real world data.
This course offers the following experiential learning activities:
Collection and manipulation of real-world data.
Empirical analysis of past data and forecasting of future economic scenarios.
Practical use of statistical software.
Teaching Methods
Classes will consist of lectures delivered in bimodal format. Some of the lectures will include tutorials and examples using statistical software or brief activities for the students to carry out on their own . The lectures will be recorded and made available to students; recordings will only be accessible via Brightspace and will be removed after 150 days.
The material covered in this course is fairly advanced, so I strongly encourage you to ask questions during the lectures or come to my office hours if there is anything that is unclear or that you would like to discuss further.
In addition, please be advised that learning how to use statistical software for the type of analysis studied during this course takes time and practice. I recommend that you start getting familiar with the software and data work as soon as possible, and I suggest that you talk to your classmates and help each other in developing the skills that you need for this part of the course.
Required Materials
Required textbook: Financial Econometric Modeling by Stan Hurn; Vance L. Martin; Jun Yu; Peter C.B. Phillips. Oxford University Press, 2020.
ISBNs: MATT0Q4LCP, 9780190857066, 9780190857073, 9780190857127, 0190857072
I might assign additional readings, for instance research papers and lecture notes; these will be communicated during the course and made available to students through the Brightspace.
Technology: For all courses offered in bimodal or online, it is mandatory to have a computer equipped with a camera. You will also need a good internet connection and a smartphone/tablet or printer and scanner.
Statistical Software: The assignments for the course will require the use of statistical software. You can choose one of the following: STATA, R, Matlab. I am most familiar with STATA and Matlab, so these are the platforms that I will use for the in-class examples and tutorials (and please also be aware that if you decide to use R and encounter some issues, I might not be able to help you).
Students can download STATA at the link provided in the Department of Economics webpage. R is available for free and can be downloaded from: https://www.r-project.org/. Matlab is available in the Vanier labs.
If you plan to use the Vanier labs from remote, I encourage you to get familiar with the remote setup as soon as possible.
Assessment Strategy
Components of Final Mark |
Date |
Participation |
Approximately weekly |
Homework assignments (3) |
Jul. 21; Jul. 28; Aug. 16 |
Midterm exam |
Aug. 4 |
Final exam |
Aug. 24 |
The final grades for the ECO4185 course will be computed based on: participation (5%); three homework assignments (10% each), one midterm exam (25%), and the final exam (40%).
Participation: Participation points can be earned by completing shorts quizzes and/or other activities concerning the material covered in class. There are a total of 7 participation points but students only need to complete 5 of them to receive the full 5% participation mark. The points are meant to be completed during the lecture time. The classes that include a participation point will be communicated in advance, and I will leave about 10 minutes at the end of the lecture for you to complete the assigned quiz/activity.
Homework: Homework assignments will consist of problems and empirical questions. The empirical questions will require the use of statistical software. The assignments are to be completed individually. There will be a total of 3 assignments for the course, each worth 10% of the overall mark (for a total of 30%).
Exams: The midterm exam will cover all material through the immediately preceding lecture, unless you are given different instructions. The final exam is cumulative. Exam questions will be drawn from the lectures, the practice material, and the homework assignments. Some of these questions might require the use of statistical software. All exams are open-book and open-notes. Further information about the format of the exams will be provided during the course. All exams will be made available to students on the Brightspace, and are to be submitted through the Brightspace by the indicated time. More detailed instructions will be given before the exams.
Bonus marks: The midterm and final exams will normally include one/two bonus questions, which give extra points if answered correctly. In addition, if you complete more than the 5 required participation points, I will count the extra ones as a bonus (1% each). Please note that no additional bonuses will be granted.
Policy on the EIN grade (incomplete):
In all economics courses, students who fail to complete work (either a single piece of work or a combination of work) worth a total of 25% or more of the final grade will receive a grade of EIN in the course. The EIN grade is equivalent to a failure mark (F). See Regulation 10.6 (https://www.uottawa.ca/administration-and- governance/academic-regulation-10-grading-system) for details.
Please note that a denied request for a deferral may therefore lead to a failure mark.
Assessment Policies and Expectations
Attendance
Class attendance is necessary to successfully complete this course. The material covered in the course is advanced (and quite technical), so I strongly recommend that students keep up with the lectures and readings during the term in order to avoid falling behind.
Time Commitment
In order to succeed in a 3-credit course, the average student should expect to spend a minimum of 6 hours (12 hours in the Summer session) per week outside of the classroom, on homework, reading, studying, etc., for a total minimum time commitment of 9 hours (18 hours in the Summer session) per week on average, per course.
Language Expectations
This course is delivered in English, and in class interactions, including the online discussion boards, and feedback will also be managed in English. As part of your evaluation will be on your writing abilities, it is recommended to take the appropriate measures to avoid mistakes such as spelling, syntax, punctuation, inappropriate use of terms, etc. You may be penalized up to 15% for poorly written materials, to the professor’s discretion.
Late Assignments
All assignments are to be submitted by their due date and time.
Quizzes and other activities counting towards participation points must be completed by the due date and time, no late submissions will be accepted. Late homework assignments will receive a penalty of 20% for each subsequent calendar day (including weekends) following the due date. For late exams, a penalty of 10% will be given for each additional 5 minutes following the indicated due date/time.
Exceptions are made only for illness or other serious situations deemed as such by the instructor. University regulations require all absences from exams and all late submissions due to illness to be supported by a medical certificate. Absence for any other serious reason must be justified in writing, to the professor, within five business days following the date of the exam or submission of an assignment.
The Faculty reserves the right to accept or refuse the reason; reasons such as travel, jobs, or any misreading of the examination timetable are not acceptable.
Missed exams and requests for deferral
Absences from exams will be penalized. Exceptions are made only for illness or other serious situations. The Faculty reserves the right to accept or refuse the reason. Conflicts due to travel, jobs, or any misreading of the
examination timetable are not acceptable reasons. Absences from exams which are not excused will result in a mark of zero for the exam.
Students who are excused for missing an exam will be required to write a deferred exam, except where the professor offers a re-weighting scheme which applies to the student’s case. (If available, such a scheme is described in this syllabus.) Except in the case of a re-weighting scheme, students wishing to be excused for an absence must complete adeferral form (DFR). This form must be completed for both midterm and final exams.
Absence due to illness must be supported by a medical certificate. Requests for deferral must be submitted, with supporting documentation (ex. medical certificate), within five working days of the exam. The request must respect all the conditions ofAcademic Regulation I9.5.
Course policy for the Midterm Exam: If a student misses the midterm exam for a valid reason, a makeup exam will be given on Friday, August 5th, 2022.
The date of the deferred final exam is September 6th, 2022, 2:30 - 5:30 p.m.
Exam conflicts
Any conflict with a midterm exam schedule should be reported to the Professor at the beginning of the
term. This request is especially applicable to the type 3 conflict (two in-class exams back-to-back) for students with special learning needs.
Any conflict with a final exam schedule should be reported to the Faculty’s undergraduate office as soon as
the final examination schedule is released.
Course Calendar
Date |
Topic |
Participation |
Assignments |
Jul. 12 |
Math review; Introductory concepts – Appendices; Ch. 1 |
point #1 |
|
Jul. 14 |
Data; The classical linear regression model – Ch. 2; Ch. 3 |
point #2 |
|
Jul. 19 |
The classical linear regression model – Ch. 3 |
point #3 |
|
Jul. 21 |
Stationary Time Series – Ch. 4 |
|
hmw 1 due |
Jul. 26 |
Stationary Time Series – Ch. 4 |
point #4 |
|
Jul. 28 |
Nonstationary Time Series – Ch. 5 |
|
hmw 2 due |
Aug. 2 |
Nonstationary Time Series – Ch. 5 |
point #5 |
|
Aug. 4 |
Midterm Exam |
|
|
Aug. 9 |
Cointegration – Ch. 6 |
point #6 |
|
Aug. 11 |
Cointegration; Forecasting – Ch. 6; Ch. 7 |
point #7 |
|
Aug. 16 |
Forecasting; GARCH models – Ch. 7; Ch. 13 |
|
hmw 3 due |
Aug. 18 |
GARCH models – Ch. 13 |
|
|
Aug. 24 |
Final Exam |
|
|
2022-07-08