University of York

What you'll study

The programme combines core theory in stochastic processes, financial economics and risk with practical training in numerical methods and programming. Teaching typically covers stochastic calculus and modelling of asset prices; derivative pricing and hedging; interest-rate and credit models; econometrics and time-series analysis for finance; and portfolio construction and risk management.

Hands-on modules develop computational skills in languages and tools commonly used in industry (for example Python, C++ or MATLAB), and introduce Monte Carlo simulation, finite-difference methods, optimisation and machine-learning techniques applied to financial data. The course culminates in an independent research project or applied industry-style dissertation that gives students the opportunity to work on real datasets or modelling problems.

• Stochastic processes and financial mathematics
• Derivatives pricing and risk management
• Computational methods for finance (simulation, PDEs, optimisation)
• Econometrics and statistical inference for finance
• Machine learning and data analytics in finance
• Fixed income and credit modelling
• Capstone project or dissertation (research or industry-focused)

Entry requirements

Applicants are expected to hold a good undergraduate degree (typically a 2:1 or equivalent) in a quantitative discipline such as mathematics, statistics, physics, engineering, computer science, economics or finance with substantial mathematical content. Successful candidates will demonstrate strong mathematical ability, familiarity with probability and calculus, and some programming experience.

Where applicants do not meet the formal subject background, the admissions team may consider relevant professional experience or evidence of quantitative training, but applicants may be advised to take preparatory modules. International applicants whose first language is not English must meet the university's English language requirements.

Career prospects

Graduates from this programme are well placed for quantitatively demanding roles across financial services and adjacent sectors. Typical career destinations include quantitative analyst (quant), risk manager, derivatives modeller, algorithmic trader, quantitative developer, data scientist in finance, and roles in asset management, investment banking, hedge funds, insurance and fintech companies.

The practical focus on programming and applied modelling means many students move directly into roles that require implementation of pricing models, development of trading models or risk systems, or data-driven research. The dissertation/industry project often provides a portfolio piece useful at interview or a route into internships and graduate jobs.

Why study at University of York

The University of York offers a research-active environment with strengths across mathematics, computing and finance. The MSc benefits from staff whose research covers stochastic analysis, financial mathematics, econometrics and machine learning, providing access to up-to-date methods and applied problems.

York’s emphasis on employability means modules include practical programming, software tools and applied projects, and students can draw on the university's careers service and employer networks to prepare for recruitment into competitive graduate roles. The university’s campus and collegiate community also offer a supportive setting for postgraduate study and collaborative work.