Quant Guide 2020: University of Toronto

Toronto, Ontario, Canada

Photo: University of Toronto

 

The Master of Mathematical Finance at the University of Toronto places seventh in this year’s Risk.net quant guide. The programme reported a 100% employment rate in finance six months after graduation of its students – up from a reported 80% in the last iteration of the guide. A large proportion of industry-affiliated instructors may also have helped it narrowly beat Carnegie Mellon.

The programme, directed by professor of mathematics Luis Seco, is also highly selective: the latest year had 560 applicants, and offers were extended to just 50, for an acceptance rate of just under 9%. Some 30 of the 50 offer holders enrolled.

The 12-month programme’s curriculum spans three sections. In the first, which runs from August to December, students tackle coursework on topics including computing, probability, maths and statistics, and attend internship interviews. In the second – January to April – students take part in an internship or an “equivalent academic project”. The latter is conducted “in the event that [a student] was unable to secure an internship”, according to programme literature. The winter section also involves the completion of a mandatory advanced course in derivatives. The third and final section – from April to July – features other advanced modules; students take classes, including operations research and risk management, as well as a series of workshops.

The faculty comprises instructors from several areas of the university, including the departments of mathematics, computer sciences, statistics, mechanical and industrial engineering, and the Rotman School of Management. Practitioner-teachers hail from companies including IBM and Nephila Capital. Two visiting professors are also currently listed on the programme: Rudi Zagst, a professor of mathematical finance from the Technical University of Munich, and Santiago Carrillo Menendez, from the Autonomous University of Madrid.

View this institution’s entry in the 2019 guide

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