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Financial Econometrics II, MFIN 705
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.
1
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. 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. 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.