Quant Guide 2020: Rutgers University

Piscataway, New Jersey, US

Photo: Nick Romanenko/Rutgers

 

Master’s in Financial Statistics and Risk Management

Rutgers University is one of several institutions with two programmes represented in this year’s Risk.net quant guide. The Financial Statistics and Risk Management master’s degree is the smaller of its two, with 23 full-time students in its latest cohort. The programme can be completed in 18 or 24 months; its academic director is professor of statistics Rong Chen. Applications are accepted on a rolling basis.

Two tracks are available to candidates: one in financial statistics, and one in financial risk management. Both have sets of mandatory modules, with some overlap, and there are unique electives available for each. A large pool of electives is also offered to students from both tracks; it includes classes in applied multivariate analysis, credit risk modelling, portfolio theory, stochastic processes, and two separate modules in programming languages and compilers.

Recent updates to the curriculum include changes to the databases elective to cover big data platforms Hadoop and Spark, says managing director of professional programmes Mohannad Aama. A new introductory course in Python has been created too, he adds, for students who don’t have strong coding experience when they join the programme. The degree also includes a data science certificate, which requires students to take an extra three data science classes.

To graduate, students must complete practical training work. All candidates take a zero-credit compulsory practical training module every semester, on top of which additional industry contact is encouraged. An off-campus internship is one option, while students who already work part-time in a financial job can also get academic credit for their efforts. The requirement can also be satisfied through the completion of a research project, or through participation in practitioner seminars and workshops.  

Master’s in Mathematical Finance

Rutgers’ second programme in the Risk.net quant guide this year is its Master’s in Mathematical Finance. Its academic director is professor of mathematics Paul Feehan. The programme has 43 students in its most recent intake, and can be completed in three semesters. Most students, however, opt to complete it in four.

Several Rutgers departments contribute to the teaching. For the core modules – two classes each in mathematical finance, econometrics and numerical analysis – staff are drawn from the departments of mathematics, statistics, and electrical and computer engineering. This faculty pool broadens once students begin their electives, to include instructors from Rutgers Business School and the departments of computer science, economics, systems engineering and operations research.

The faculty’s industry practitioners include: Elliot Noma, founder of Garrett Asset Management; Alexander Shklyarevsky, director of model risk management at AIG; and Paisan Limratanamongkol, head of quantitative research at Citi Investment Management.

Students can also obtain several certificates. One is the data science certificate available to those on the Financial Statistics and Risk Management stream. Another, a certificate in financial statistics and risk management, is exclusive to the Mathematical Science master’s and the Data Science master’s. The third, a certificate in mathematical finance, is available to all students minus those on the Statistics master’s.

View this institution’s entry in the 2017 guide

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