Quant Guide 2019: University College London

MSc in Computational Finance, MSc in Financial Risk Management | metrics table at end of article

Computational finance

UCL’s computational finance programme combines traditional mathematics, a financial component and a focus on computation and statistics. The curriculum has undergone recent change, adding new modules in machine learning, algorithmic trading and systemic risk. The programming languages element has also been updated.

Although the degree is housed in the computer science department, there is a strong emphasis on mathematics. The programme director, Guido Germano, explains that many undergraduates choose bachelor’s degrees in career-orientated subjects such as finance or business, which have a less rigorous maths component. When they arrive on the master’s, students are offered maths refresher courses to consolidate their skills.

“Machine learning requires a mathematical and statistical knowledge many don’t appreciate,” he says. “You need the mathematical basis to understand what’s happening with machine learning in the same way you would with pricing or data analysis.”

During the one-year programme, students have the chance to take part in a summer internship, through which they gain practical experience in the financial industry. Sponsor firms include technology vendors, asset managers, hedge funds and investment banks – although Brexit-related uncertainty has caused several large banks to pull out of the scheme.

Germano is not unduly worried at this development. Big banks, he says, are increasingly perceived by some students as old-fashioned or dull compared with finance’s more dynamic and fashionable fintechs and buy-side firms.

“Students are excited about blockchain and crypto projects, which aren’t offered by the majors,” he says. “They’re not interested in [building] another Monte Carlo VAR framework or making a small change to a C# pricing library for over-the-counter exotic derivatives.”

The programme’s graduate employment rate is at 90% between 2014 and 2018. Germano points out that around 30% of students that do industrial placements are hired by the companies that hosted them as interns, a figure he feels could be higher.

“We have a very large number of students from overseas,” he says. “And there are often visa restrictions, so I wouldn’t say that 30% is a bad number – it would need to be normalised on the amount of students who are actually able to stay in London. Given that our international component is about 80%, I would say it’s a good percentage.”

Financial risk management

The MSc is one of a small group of degree programmes with a specific risk management focus in this year’s guide. Students are trained to analyse risk using quantitative methods, testing their programming and computing skills.

The programme director, Fabio Caccioli, says the informal course philosophy is that students should understand the underlying hypotheses of any apparatus or technique they employ. Tomorrow’s risk managers will use machines to perform an increasing number of functions, but this reinforces the importance of human involvement in two areas: setting the parameters for the computers, and testing their outputs.

Caccioli says: “Students have got to understand both the technology itself and its limitations. They can’t trust anything that comes out of the computer at face value. You’ve got to be sure that whatever model you’re developing, or whatever data you put into the computer, complies with the underlying assumptions relied on by the machines you’re using.”

The programme is structured similarly to the computational finance degree, with core modules, optional modules and a dissertation, all equally weighted. The thesis is typically based on research that students undertake during their summer internship.

Core courses cover material ranging from statistical analysis and probability to market risk, exotics and stochastic calculus. Optional modules range from algorithmic trading, applied computational finance, financial institutions and markets, and machine learning to market microstructure, numerical methods and two courses on operational risk.

The size of the op risk offering, which is something of a rarity among the programmes represented in the guide this year, is for Caccioli a necessary measure given the size of conduct-related losses incurred by banks since the financial crisis. The industry also faces heightened uncertainty in 2018 and beyond.

“The political situation, everywhere – it’s unexpected. The future of the US, the future of Europe – old thinking doesn’t apply any more,” he says.

Stronger post-crisis regulation has redefined the role of risk management, and students are exposed to the varied effects of these new regimes.

“In our systemic risk course we teach techniques to perform stress tests,” says Caccioli, “which helps students look at the financial system from the regulatory perspective. We make the point that regulation sometimes increases unpredictability – you might have situations where individuals trying to reduce their own risk create risk for the system.”

During the three-month summer project, students tackle real industrial problems at a range of financial companies. Each student’s progress is monitored by an industrial supervisor and an academic adviser, who subsequently assesses the dissertation the student produces.

Modes of assessment of this type help to prepare students for the demands and realities of financial practice, says Caccioli – gradual exposure can help ease the often jarring transition from theory-dominated academia to a graduate position, where a solid grasp of practical methods is needed.

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