Quant Guide 2020: Johns Hopkins University

Baltimore, Maryland, US

 

 

Baltimore-based Johns Hopkins University makes its quant guide debut this year. Its Financial Mathematics Master’s programme is led by professor of applied mathematics and statistics Daniel Naiman and is completed over three semesters.

Students can shape their degree programme by selecting an area of focus, from which a sequence of modules reflecting their selection is designed by programme staff. Students can choose from a list of focus topics including risk management, asset management, derivatives, quantitative, algorithmic and high-frequency trading, as well as fixed income and commodities. The derivatives focus, for instance, contains seven modules, including a class on Monte Carlo methods, a class on stochastic processes and their applications in finance, a class on financial engineering and structured products, and a class in advanced equity derivatives.

The master’s programme has undergone some significant revisions in the past year, says programme co-ordinator for maths and statistics Lisa Wetzelberger. The financial computing course is now completely Python-based and a revamped winter computing workshop now focuses on high-frequency trading and market microstructure modelling. Recent additions to the programme include three new courses in financial mathematics with a “substantial” computational element: a dedicated financial computing class; a quantitative equity trading class; and an exotic derivatives class.

The master’s maintains a strong practical focus and the faculty is host to several former industry executives, including applied mathematics professor David Audley, an experienced fixed income trader; research professor Hélyette Geman, known for her work on commodity derivatives; and lecturer John Miller, who worked as chief risk officer for Credit Suisse’s equity division.

There has been a “marked shift” in graduate employment routes, adds Wetzelberger. More and more students are now finding jobs at buy-side or financial analytics firms, she observes, as opposed to sell-side investment banks. 

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