Quant Guide 2017: University of Washington

Seattle, USA

MS in Computational Finance and Risk Management | metrics table at end of article

Not many quant finance master’s programmes are available to those who prefer distance learning.

“We usually have 30–35 students on-campus. Our biggest limiting factor is space,” says Matt Austin, assistant director of the University of Washington’s master’s in computational finance and risk management programme, which was founded in 2011.

The online learning option, with around 25 students currently enrolled, was introduced to help address this constraint. It is popular with mid-career finance professionals who want to advance their quantitative skills, says Austin, and who tend to be employed full-time, making it difficult to attend campus-based lectures. It usually takes them three years to complete the master’s. The full-time option is available, too, which means students would need to complete more than one course per quarter.

The online version is taught at the same time as its campus-based version by the same instructors, and involves the same assignments. The principal difference between the two study modes is that on-campus students can use face-to-face career placement services more efficiently, says Austin. Students from both cohorts are encouraged to interact via email, web conferencing and course discussion forums.

“We have a very strong focus on practical skills development, and that includes some professional development throughout the year,” says Austin.

The staff features a mix of academics and industry practitioners, which helps the faculty keep pace with the changing priorities of the financial industry. The programme has a framework that enables administrators to put together a course on a special topic at very short notice; a new course can be launched in two to three months. In comparison, it takes about two years to establish a permanent course.

The topics of the flexible course are rotated, depending on what presents interest at a given point in time. Junior researchers are encouraged to contribute to the syllabus by developing elective courses on the basis of their research interests.

Students are required to complete seven compulsory modules: two on investment science; financial data modelling and analysis; portfolio optimisation and asset management; options and other derivatives; financial risk management; and fixed-income analysis and portfolio management.

The range of optional courses is wide: students can choose a course on Monte Carlo methods in finance, electronic trading, financial time series forecasting, energy markets or stochastic calculus, among others.

A new course on machine learning in finance will be introduced this autumn as an extension of the existing data analysis course, introducing some more distributed computing and neural networks, as well as artificial intelligence for those students who are interested in going into fintech.

Some of the students enrolled in this programme are active members of the local CFA society. They have participated in the CFA Research Challenge and the CQA Investment Challenge.

The programme can also be studied in the form of two non-degree certificate programmes, designed for those who are considering enrolling in the main programme but aren’t necessarily prepared to commit to becoming a master’s student right away. If they decide to continue their studies, all of the covered modules count towards their final qualification.

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