Quant Guide 2017: LSE

London, UK

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MSc in Financial Mathematics | metrics table at end of article

LSE, or the London School of Economics and Political Science, offers a one-year master’s programme in financial mathematics – a collaboration between the departments of mathematics, statistics and finance. The programme is targeted at students with backgrounds in mathematics; no previous computing experience is required. 

In 2014, 24 students were admitted out of 586 applicants, according to the LSE’s website. A spokesperson for the institution – which was unable to provide the requested metrics on staff ratios, research publications and citations or graduate employment rates – confirms the current 2016–17 intake is 25 students out of 683 applications.

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Students are assessed in up to eight courses, five of which are compulsory. These cover Black-Scholes theory, interest rate and credit risk theory, stochastic processes, fixed-income markets, and computational methods in finance. Electives include game theory, portfolio management and probabilistic methods in risk management. 

There is also the option to attend lectures on current research topics. 

The computational course is spread across two terms and focuses on C++. Students take part in a total of around 22 hours of computer workshops, plus an optional 10 hours in the forecasting financial time series course. 

There is one compulsory computational project, to be completed in the summer, plus further projects – not necessarily computational – for students taking elective courses in, for example, Markov processes or portfolio management.

Several seminars are hosted by the university, including some that aim to promote discussion of financial mathematical research. The LSE also co-hosts the London mathematical finance seminars series, in collaboration with other London universities such as Imperial College London. 

Prior to the first term in late September, students must complete a compulsory two-week pre-sessional course to prepare them for later courses. It introduces topics in, for example, probability.

LSE metrics

Click here for links to the other universities and an explanation of how to read the metrics tables

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