Loughborough University

What you'll study

This master's combines rigorous mathematical theory with practical computational techniques used in modern finance. Core themes include stochastic calculus, derivative pricing, numerical methods for partial differential equations, Monte Carlo simulation and statistical techniques for time series and volatility modelling.

• Stochastic Processes and Stochastic Calculus – foundations of Brownian motion, Itô calculus and martingale methods applied to finance.
• Derivative Pricing and Risk Neutral Valuation – Black–Scholes framework, incomplete markets and pricing under different modelling assumptions.
• Computational Methods in Finance – finite-difference and Monte Carlo methods, variance reduction, calibration and implementation considerations.
• Numerical Analysis and PDE Techniques – numerical solution of pricing PDEs, stability and convergence, and high-dimensional problems.
• Time Series and Econometric Methods – model selection, GARCH-type models, forecasting and applications to volatility modelling.
• Machine Learning and Data Techniques for Finance (optional) – supervised and unsupervised methods, feature engineering, and applications to algorithmic trading and risk prediction.
• Programming and Software for Quantitative Finance – practical training in languages and libraries commonly used in the industry (for example Python, C++ or MATLAB), code optimisation and model implementation.
• Research Project / Dissertation – an independent piece of work applying mathematical and computational tools to a financial problem, often conducted with access to industry data or in collaboration with academic supervisors.

Most modules are assessed through a mix of coursework, programming exercises, written exams and the final dissertation. The programme normally offers a combination of compulsory core modules and optional modules so you can tailor studies towards numerical analysis, modelling or data-driven finance.

Entry requirements

Typical applicants will hold a good undergraduate degree with substantial quantitative content. Admissions commonly expect a recognised upper second-class honours degree (2:1) or international equivalent in mathematics, statistics, physics, engineering, actuarial science, quantitative economics or a closely related subject.

• Mathematical background: comfort with calculus, probability theory, linear algebra and basic real analysis is expected. Candidates without a degree in a directly related subject may be considered if they can demonstrate equivalent quantitative training or relevant professional experience.
• Programming experience: prior exposure to programming and numerical computation (for example in Python, C++, MATLAB or R) is beneficial and may be asked for on application.
• English language: applicants whose first language is not English must show proficiency through a recognised qualification. The programme requires the standard level normally expected for postgraduate study at Loughborough University.
• Further information: selection may consider transcripts, references and a personal statement; some applicants may be invited to interview or asked to provide examples of quantitative work.

Career prospects

Graduates are well placed for quantitative roles across financial services, technology and research. Typical career destinations include:

• Quantitative analyst (front-office model development and pricing)
• Risk analyst and model validator in banks and regulatory environments
• Quant developer or software engineer implementing pricing and risk systems
• Portfolio analyst, asset management and algorithmic trading roles
• Data scientist roles where strong probabilistic and numerical skills are required
• Further research or doctoral study in financial mathematics, statistics or computational mathematics

The programme’s emphasis on both theory and implementation helps graduates move directly into roles that require model development, model implementation and critical model assessment.

Why study at Loughborough University

Loughborough has a strong tradition in mathematics and applied mathematics, with academic staff active in applied probability, numerical analysis and computational finance. The department combines rigorous teaching with research-led modules and opportunities to work on substantial project topics under experienced supervisors.

• Industry links and employability support: the university maintains connections with financial and technology employers, and the careers service provides support on interview preparation, internships and employer events.
• Practical computing facilities: access to high-performance computing resources, software licences and specialised labs allows you to develop and test computationally intensive models.
• Strong campus environment: Loughborough’s campus-focused university life offers easy access to academic support, study groups and societies that can enrich the learning experience.
• Research-informed teaching: course content is shaped by active research in stochastic analysis, numerical methods and data-driven finance, ensuring up-to-date material and contemporary project topics.