Master of Mathematical Finance | metrics table at end of article
Ranked 9th in the Quant Finance Master’s Guide 2019
While many schools are happy to chase prospective students and boost graduate numbers, one professor is determined to keep his mathematical finance master’s programme small and manageable. The Illinois Institute of Technology (IIT) launched the degree in 2004 with just four students, and cohort sizes have risen as high as 33, which programme director Tomasz Bielecki sees as too many.
“We want to keep it as a boutique-type programme, one that fits the structure of the departments and the business school. I don’t want to make my programme big and unwieldy,” he says.
All students take eight compulsory classes plus three electives chosen from a pool of applied maths, computer science and finance classes. The eight core courses include introductory mathematical and computational finance, stochastic processes and fixed income modelling. Electives in data mining, machine learning and artificial intelligence have proven popular.
Having access to faculty from both the applied mathematics departments and IIT’s Stuart School of Business is an important advantage for the institution’s quant finance offering, says Bielecki.
“Our faculty is strong on theory,” he says, “but we also have a lot of practical experience.”
One such industry link is Andrei Lyashenko, head of quantitative research at risk management consultancy QRM, who teaches fixed income modelling as an adjunct. Bielecki himself has worked for hedge funds in credit risk modelling.
As with many quant finance postgraduate programmes, the course has evolved in response to changing markets and industry requirements. During the years of the financial crisis, with counterparty risk to the fore, a key focus of the programme was credit risk management and credit derivatives. Since then, course topics have shifted to quantitative risk management, along with artificial intelligence and the application of neural networks.
Outside the classroom, the programme offers summer placements. Many students opt to extend these industry placements and stretch the overall programme across four semesters. Industry partners include exchanges CME and Ice.
“We’ve been lucky enough to place our students in all sorts of offices – trading companies and hedge funds, but also software developers who work for financial companies, risk management firms and insurance companies,” says Bielecki.
Despite the raft of financial avenues available to students when they intern, they’re not necessarily leaping into finance straight away upon graduating, points out Bielecki.
“They look around,” he says. “It typically starts with one student – one goes, then [the company] takes another one and another one. They like to steal grads in this area. For us, it’s primarily Microsoft.”
Tech-sector interest in the financial space is, for Bielecki, a natural consequence for a field which becomes more intricate year on year.
“The market scenery has changed considerably since the programme started,” he says. “Initially we focused on the pricing and hedging of derivatives; nobody was talking about the need for margining in CCPs, for instance, years ago.
“Of course, there is a certain core of knowledge students need to get, and that will not change. The mathematical basis has to be there. But depending on what’s needed and how employment opportunities shape up, we will adapt ourselves.”
To keep pace with industry demands, Bielecki says, IIT staff “both on the math side and on the business school side” participate in frequent meetings with practitioners throughout the year.
Maintaining these links is hard amid a growing divide between academia and practice, Bielecki notes: “There’s almost an abyss between the two groups.” One problem, he says, is that academics too often become absorbed in heady theorising and lose focus on practical work. “Especially in the US, people pretend to do research in mathematical finance, but they’re really just doing pure maths.”
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