Master Of Quantitative Finance (MQF)

Based in the Department of Statistics and Actuarial Science, this program is built upon the Finance program established in 1995 by founding director, renowned researcher Phelim P. Boyle.

The Master of Quantitative Finance (MQF) program focuses on the fundamental disciplines of mathematics, statistics, econometrics, machine learning, computer science and finance. It provides the analytical tools to solve practical problems in the complex and rapidly evolving world of today's financial industry.

Graduates from the Master of Quantitative Finance are equipped for positions with financial institutions, corporations and government regulatory organizations.

Careers Include:

• Financial Engineers in model vetting and risk compliance
• Analysts in portfolio credit risk, market risk, investment banking and auditing
• Asset Managers and Portfolio Managers
• a range of positions requiring advanced skills in quantitative finance

MQF Admission Requirements:

• Academic background requirements
• Students from various disciplines are invited to apply for admission. Successful applicants have had backgrounds in quantitative fields such as actuarial science, computer science, economics, engineering, mathematics, physics, and statistics.
• Submission of GMAT and/or GRE scores is optional and not required.
• If you are applying to the Master of Quantitative Finance, then your background should closely match the following:
• General requirements
• A four-year honours degree (or equivalent) in a quantitative discipline with at least an 80% average
• Strong analytical skills and ability to handle rigorous mathematical analysis
• Programming skills: Python, Matlab, R, or C++
• Superior verbal and written communication skills
• General knowledge of financial markets and economics
• Demonstrated interest in current developments in finance
• Work or co-op experience is an asset 
• Specific requirements
• Our program involves a very high level of mathematical rigour and we expect our students to have a solid background in mathematics. Specifically, applicants should be familiar and comfortable with formal, proof-oriented mathematical reasoning and they should be proficient in all of the following subjects:
• Single and multivariable calculus
• Real analysis
• Linear algebra
• Advanced undergraduate probability
• Advanced undergraduate statistics
• Proficiency in these subjects is typically documented through strong grades in corresponding courses. At the University of Waterloo, these would typically include the courses PMATH 351 (Real Analysis) or PMATH 331 (Applied Real Analysis) as well as STAT 333 (Stochastic Processes 1) and STAT 330 (Mathematical Statistics). 
• We recommend a working knowledge of at least one programming language such as Python. Other useful languages are VBA, SQL, or C++.