**Sorry, we are not accepting applications to the Math Finance
program at this time. If you are still interested in financial mathematics next year
please get back in contact then**
The Department of Mathematics in conjunction with the
Institute of Applied Mathematics (IAM) offers an interdisciplinary
M.Sc. program in Mathematical Finance. This 20 month program trains
qualified students for either a career in the finance industry,
or further study leading to the Ph.D. degree. Students can build their expertise in
quantitative finance through a combination of core courses in mathematical finance
combined with mathematical, comptutational, statistical courses as listed below.
The program provides a number of special features that give its students an edge:
Internship component providing practical industrial training in quantitative finance
Multidisciplinary curriculum
that combines quantitative training together with financial background.
The program provides access to complementary mathematical, computational, and
statistical training, providing great flexibility for understanding today's markets.
Development of professional skills through workshops
Close contact with industry members of the program's Advisory Board and beyond
In cooperation with MITACS,
Mathematics for Information Technology and Complex
Systems, the program includes an industrial internship component. Each student in the program is expected to do a four month internship as part of their training. As part of the preparationi for the internship, each student is required to participate in at least two professional development/soft skills workshops offered by MITACS and to collaboratively submit
the proposal for the research internship together with the company representative(s).
The program is offered jointly by the Departments of
Mathematics and Statistics (Faculty of Science) and the Finance Division
(Faculty of Commerce and Business Administration). Students may apply to
enter the program either through the Division of Finance or through the
Mathematics or Statistics Departments. In addition to the requirements
listed below, students must satisfy the general requirements of the
Faculty of Graduate Studies and any applicable Departmental or Divisional
requirements.

ADDITIONAL INFORMATION FOR APPLICANTS

Applicants must have a strong academic background
showing good analytical skills and a Bachelor degree. In particular,
students entering through the Mathematics Department are expected to have
the equivalent of MATH 320 (Real Variables I), and MATH 321 (Real
Variables II) as well as some elementary knowledge of ordinary and partial
differential equations (e.g. MATH 215 (Elementary Differential Equations
I) and MATH 316 (Elementary Differential Equations II)) and statistics
(e.g. STAT 305 (Introduction to Statistical Inference)). Brief course
descriptions can be found at MATH and
STAT.
Applicants are expected to be able to write computer code. The course CPSC
122 provides this skill. Applicants from nonanglophone countries must
obtain a TOEFL score of at least 600 with a TWE score of 5.0. The GRE exam
is not required. To apply to this program through the Mathematics Department
go to its web page and follow
the links "Prospective Students" and "Online Application". To apply through
another department, contact that department. 
PROGRAM REQUIREMENTS 
Students must take at least 30 credits of course work
and write an essay.

Core Courses 
(must be taken) 
COMM 671 Theory of Finance,
COMM 673 Advanced Topics in Theoretical Asset Pricing,
MATH 605D Topics in Applied Mathematics  Mathematical Finance Theory
MATH 607D Topics in Numerical Methods  Stochastic Processes in Financial
Applications.

In addition to these core courses, students can choose from the
following courses and can design their program with a mathematical, computational, statistical focus or they may select a combination of courses from different focus areas.

SOME RECOMMENDED ELECTIVES

Statistical Focus 
Capstone courses:
STAT 543 Time Series Analysis;
STAT 560/561 Statistical Inference
Other electives:
STAT 530 Bayes Inference and Decision; STAT 538 Generalized Linear Models;
STAT 541 Applied Multivariate Analysis;

Computational Focus 
Capstone courses: CPSC 542 Topics in Numerical Computation: Nonlinear Optimization
Other electives
MATH 521 Numerical Analysis; CPSC 517 Sparse Matrix Computation;
CPSC 520 Numerical Solution of Differential Equations.

Mathematical Focus 
Capstone courses: MATH 606 Topics in Differential Equations:
Models from Mathematical Finance
Other electives Math 507 Measure Theory; Math 544/545 Probability

MultiAgent Systems 
Capstone courses:
CPSC 532 Topics in AI: MultiAgent Systems;
CPSC 564 Data Mining
Other electives
CPSC 500 Algorithms; CPSC 502 Artificial Intelligence; CPSC 504 Data Management;
CPSC 540 Machine Learning.

Economics and Finance 
Economics :
ECON 527 Econometric Methods;
ECON 590A Advanced Topics in Economics;
Finance
COMM 583 Forecasting/Time Series in Business Environments; COMM 586;
COMM 674 Advanced Topics in Empirical Asset Pricing;
COMM 695 Advanced Topics in Empirical Corporate Finance;
COMM 612 Advanced Optimization;
COMM 616 Optimization Theory and Applications

OTHER PROGRAM REQUIREMENTS 
(must be taken if equivalent course not taken previously
) 
Microeconomics such as ECON 500,
Probability at the level of MATH 418,
Stochastic Processes such as MATH 419.
Numerical Methods for Differential Equations such as Math 607E.
Statistics prerequisites in regression and time series analysis may be required for courses below.

Additional Program Information 
Students registered in the Finance Division may choose
some MBA modules as Electives. 
Students registered in Mathematics will follow the
program under the auspices of the Institute of Applied Mathematics.
The
IAM Graduate Student Handbook provides useful information on requirements. 
The complete program must be approved by a program
advisor. 
Participating Mathematics Faculty

Martin Barlow
Ivar Ekeland
Ulrich Haussmann
Rachel Kuske
John Walsh

Participating Finance faculty include Ali Lazrak and Tan Wang.
