McMaster University
DeGroote School of Business
Financial Econometrics II, MFIN 705
Course Outline
Prof. John M. Maheu 2023
Office: DSB-305
Office Hours: by appointment
Phone: 905-525-9140 ext. 26198
Class Times: C01 Tues 2:30-5:20, BSB B103
Email: maheujm@mcmaster.ca
Homepage: http://avenue.mcmaster.ca/
TA: Hamidreza Khoshro, email: khoshroh@mcmaster.ca
TA Office Hours: TBA
Prerequisite: MFIN 701
Course Description:
This is a second course in financial econometrics focused on applications to various finan-
cial problems. Topics will include cointegration, vector autoregression models, univari-
ate and multivariate volatility models, simulation methods, Markov switching models,
methods for big data and machine learning models and concepts. Inference will focus
on likelihood based approaches including maximum likelihood and Bayesian methods.
Grading:
50% Assignments
40% Project, due end of term, exact date TBA
10% Participation
Late assignments or term project will have 10% deducted per day late.
Conversions:
At the end of the course your overall percentage grade will be converted to your letter
grade in accordance with the following conversion scheme.
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Letter grade Percent Points
A+ 90 - 100 12
A 85 - 89 11
A- 80 - 84 10
B+ 75 - 79 9
B 70 - 74 8
B- 60 - 69 7
F 00 - 59 0
Course Textbook:
Analysis of Financial Time Series, by Ruey Tsay, second edition, John Wiley &
Sons
The text is available online as a hardcover and ebook. A pdf of the book is on Avenue as
well. Detailed lecture notes and R software examples will be posted on the class website.
Additional Resources:
Pattern Recognition and Machine Learning, by Christopher M. Bishop, Springer,
2016.
Time Series Analysis, by James D. Hamilton, Princeton University Press, 1994.
Computer Assignments:
Students will complete computer assignments using R (or equivalent, Ox, Gauss, Matlab
etc) econometric package. A personal version of R can be obtained free of charge from
http://cran.r-project.org/. See the course website for links to R including downloading
and documentation. Rstudio is an R interface that can be used to program and run R
jobs from. It can be downloaded at https://www.rstudio.com/. Computer programming
applications will be discussed extensively in class. Students can work together on the
computer programming and model estimation but the final write-up of an assignment
should be done independently. If plagiarism is detected University rules will be enforced.
Assignments must have a detailed write-up of results and be separate from
computer output. NO R OUTPUT SHOULD APPEAR IN YOUR FOR-
MAL WRITE-UP. Significant marks will be deducted when this occurs. R
code and output is to support your formal write-up only.
Term Project:
Students are required to complete an applied econometric project based on a finance
topic of their choice. Please feel free to discuss the suitability of your topic with me. In
selecting a topic it may be helpful to look at current and past periodicals on econometrics
in the library or online through the library web page. Some suggested sources are:
1. Journal of Financial Econometrics
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2. Journal of Business and Economic Statistics
3. Journal of Empirical Finance
4. Review of Economics and Statistics
5. Journal of Applied Econometrics
Your paper can be completely original or you can base it on existing work using a
different dataset and changing and/or expanding the analysis.
The term paper should consist of an Introduction, Model Description, Results, and
Conclusion with References included. The main text should be 10 pages or less.
All mathematical equations should be written properly in the text. As an example,
consider the AR(1) model,
yt = µ+ ϕyt−1 + ϵt, ϵt ∼ N(0, σ2),
where µ, ϕ, and σ2, are parameters to be estimated.
Data sources should be included, along with footnotes, and correct citations. Using
someone’s idea or writings without a citation is plagiarism and University rules will be
enforced. Your paper should be self contained. Finally, you should hand in:
1. pdf of your paper,
2. plain text file of computer code,
3. dataset,
4. R output from running your code.
Topics to be covered:
1. Review of some statistical concepts: probability, common distributions.
2. Likelihood based inference: maximum likelihood estimation and Bayesian estima-
tion
3. ARMA models
4. Vector autoregressions
5. Cointegration and error correction models
6. Models of volatility
7. Simulation methods: computation of value-at-risk and expected loss.
8. Mixture models and their uses
9. Markov switching models
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10. Limited dependent variable models
11. Big data methods
12. Machine learning: neural networks
13. Other topics
Communication and Feedback:
Students who wish to correspond with instructors or TAs directly via email must send
messages that originate from their official McMaster University email account. This
protects the confidentiality and sensitivity of information as well as confirms the iden-
tity of the student. Emails regarding course issues should NOT be sent to the Area
Administrative Assistants.
Requesting Relief for Missed Academic Work:
In the event of an absence for medical or other reasons, students should review and
follow the Missed Term Work regulations that our outlined on the Master of Finance
website;
https://mfin.degroote.mcmaster.ca/current-students/missed-term-work/
Academic Integrity:
It is the student’s responsibility to understand what constitutes academic dishonesty.
Please refer to the University Senate Academic Integrity Policy at the following URL:
http://www.mcmaster.ca/policy/Students-AcademicStudies/AcademicIntegrity.pdf
This policy describes the responsibilities, procedures, and guidelines for students and
faculty should a case of academic dishonesty arise. Academic dishonesty is defined as to
knowingly act or fail to act in a way that results or could result in unearned academic
credit or advantage. Please refer to the policy for a list of examples. The policy also
provides faculty with procedures to follow in cases of academic dishonesty as well as
general guidelines for penalties. For further information related to the policy, please
refer to the Office of Academic Integrity at:
http://www.mcmaster.ca/academicintegrity
Authenticity/Plagiarism Detection:
Some portions of the course may require a web-based service (Turnitin.com) to reveal
authenticity and ownership of student submitted work. For courses using such software,
students will be expected to submit their work electronically either directly to Tur-
nitin.com or via an online learning platform (e.g. A2L, etc.) using plagiarism detection
(a service supported by Turnitin.com) so it can be checked for academic dishonesty.
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Students who do not wish their work to be submitted through the plagiarism detec-
tion software must inform the Instructor before the assignment is due. No penalty will
be assigned to a student who does not submit work to the plagiarism detection software.
All submitted work is subject to normal verification that standards of academic
integrity have been upheld (e.g., on-line search, other software, etc.). For more details
about McMaster’s use of Turnitin.com please go to www.mcmaster.ca/academicintegrity.
Conduct Expectations:
As a McMaster student, you have the right to experience, and the responsibility to
demonstrate, respectful and dignified interactions within all of our living, learning and
working communities. These expectations are described in the Code of Student Rights
& Responsibilities (the “Code”). All students share the responsibility of maintaining a
positive environment for the academic and personal growth of all McMaster community
members, whether in person or online.
It is essential that students be mindful of their interactions online, as the Code
remains in effect in virtual learning environments. The Code applies to any interactions
that adversely affect, disrupt, or interfere with reasonable participation in University
activities. Student disruptions or behaviours that interfere with university functions on
online platforms (e.g. use of Avenue 2 Learn, WebEx or Zoom for delivery), will be taken
very seriously and will be investigated. Outcomes may include restriction or removal of
the involved students’ access to these platforms.
Academic Accommodation of Students with Disabilities:
Students with disabilities who require academic accommodation must contact Student
Accessibility Services (SAS) at 905-525-9140 ext. 28652 or sas@mcmaster.ca to make
arrangements with a Program Coordinator. For further information, consult McMaster
University’s Academic Accommodation of Students with Disabilities policy.
Academic Accommodation For Religious, Indigenous or Spiritual
Observances (RISO):
Students requiring academic accommodation based on religious, indigenous or spiritual
observances should follow the procedures set out in the RISO policy. Students should
submit their request to their Faculty Office normally within 10 working days of the be-
ginning of term in which they anticipate a need for accommodation or to the Registrar’s
Office prior to their examinations. Students should also contact their instructors as soon
as possible to make alternative arrangements for classes, assignments, and tests.
Missed Academic Work
Late assignments will not be accepted. No extensions are available except under extraor-
dinary circumstances. Please discuss any extenuating situation with your instructor at
the earliest possible opportunity.
Potential Modifications to the Course:
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The instructor and university reserve the right to modify elements of the course during
the term. The university may change the dates and deadlines for any or all courses
in extreme circumstances. If either type of modification becomes necessary, reasonable
notice and communication with the students will be given with explanation and the
opportunity to comment on changes. It is the responsibility of the student to check
their McMaster email and course websites weekly during the term and to note any
changes.
Copyright and Recording
Students are advised that lectures, demonstrations, performances, and any other course
material provided by an instructor include copyright protected works. The Copyright
Act and copyright law protect every original literary, dramatic, musical and artistic
work, including lectures by University instructors.
The recording of lectures, tutorials, or other methods of instruction may occur during
a course. Recording may be done by either the instructor for the purpose of authorized
distribution, or by a student for the purpose of personal study. Students should be aware
that their voice and/or image may be recorded by others during the class. Please speak
with the instructor if this is a concern for you.
Extreme Circumstances
The University reserves the right to change the dates and deadlines for any or all courses
in extreme circumstances (e.g., severe weather, labour disruptions, etc.). Changes will be
communicated through regular McMaster communication channels, such as McMaster
Daily News, A2L and/or McMaster email.
Acknowledgement of Course Policies
Your enrolment in Finance 705 will be considered to be an implicit acknowledgement
of the course policies outlined above, or of any other that may be announced during
lecture and/or on A2L. It is your responsibility to read this course outline, to familiarize
yourself with the course policies and to act accordingly.
Lack of awareness of the course policies cannot be invoked at any point during this
course for failure to meet them. It is your responsibility to ask for clarification on any
policies that you do not understand.