Quant Guide 2017: University of Oxford

Oxford, UK

MSc in Mathematical and Computational Finance, MSc in Mathematical Finance | metrics table at end of article

Mathematical and computational finance

Oxford University’s full-time MSc in mathematical and computational finance, launched in 2007, was overhauled three years ago in response to the shifting balance of power in financial markets and the growing demand for quants outside the banking industry.

“We found our previous curriculum was mostly for the sell side,” says Hanqing Jin, the director of the programme.

What remains unchanged in this 10-month degree is the heart of the first-term curriculum, which still revolves around the fundamentals of financial markets and modelling. 

In the second term, students now have a wider range of options. The analytical tools stream is compulsory, focusing on (among other topics) numerical methods, optimisation and stochastic control. Students can then choose one of the two other streams. The modelling stream focuses on exotics pricing, stochastic volatility and specific asset classes. The data-driven stream includes machine learning, market microstructure, inefficient markets and algorithmic trading, among other topics.

The programme usually accommodates around 30 students, but there are plans to increase the intake slightly in the coming years.

According to Jin, the strongest aspects of this degree are its mathematical rigour and the emphasis on numerical methods and data analysis. 

Students use C++ in two courses, and they receive an overview of MatLab during the introductory week. R+ is another programming language integrated into the syllabus. The programme administration is planning to introduce Python next year.

Mathematical finance

The MSc in mathematical finance was launched in 1999, and is only available part-time. As a result, of the 30 students currently enrolled in the programme, the vast majority have full-time jobs in the financial sector, and around half already have a PhD. 

Another feature that professionals may find beneficial is the fact that the curriculum is customisable: students can choose three advanced modules based on their professional interests, and write their dissertation in an area relevant to their career. Each of the advanced modules looks into a topical area in modern mathematical finance. 

The core modules cover the mathematical foundations of probability, statistics and partial differential equations, stochastic calculus and martingale theory, portfolio theory, the Black-Scholes model and extensions, numerical methods (finite differences and Monte Carlo), interest rate modelling, stochastic optimisation, exotic derivatives, and stochastic volatility. 

The programme has experienced some changes in its curriculum since its launch. “When we started, there was an awful lot of mathematics, and now there’s a lot of risk management involved,” says Jeff Dewynne, the director of the part-time master’s degree. The core features, however, have remained the same.

It currently takes around 28 months to complete the programme’s series of eight-week-long modules – a practical barrier to the number of modules that can be studied, says Dewynne. The programme seeks to stay in touch with the market’s needs via a supervisory committee, which meets every four years to set a new development trajectory for the curriculum.

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