MSc Mathematical Finance

Programme structure

The MSc Mathematical Finance course combines compulsory and optional modules across the three terms of your one-year course, covering the four key pillars of the core skill set needed for a career in the finance industry: Financial Statistics, Financial Mathematics, Asset Pricing and Risk, and Simulation and Machine Learning for Finance. Alongside this, you'll learn programming for Quantitative Finance, focusing on C++, Python, and R.

Our modules

In your three terms at WBS you will study seven compulsory modules, with optional modules enabling you to personalise the course to your own interests, allowing you to focus on your future career path in finance.

How you'll be assessed

Assessment is a mix of exams and coursework with your dissertation bringing all your learning together at the end. 

Programming for Quantitative Finance

Develop and support the skills required for practical applications of theoretical concepts in the MSc Mathematical Finance course.

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Stochastic Calculus for Finance

Get a thorough introduction into discrete-time martingale theory, Brownian motion, and stochastic calculus, illustrated by examples from Mathematical Finance.

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Financial Statistics

Discover the main approaches to statistical inference and financial time series.

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Simulation & Machine Learning for Finance

Gain a theoretical and practical understanding of numerical methods in finance

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Asset Pricing & Risk

An introduction to modern theories of Asset Pricing and Portfolio Theory in both static and dynamic settings.

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Financial Econometrics

Get an introduction to the main tools and approaches to estimation and inference of financial and economic models. 

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Applications of Stochastic Calculus in Finance

Gain a thorough understanding of how stochastic calculus is used in continuous time finance. You will also develop an in-depth understanding of models used for various asset classes. 

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Dissertation

The 10,000 word dissertation allows you to synthesise, apply and extend the knowledge you have gained in the taught component of the programme, and to demonstrate mastery of some elements of financial mathematics. 

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These are indicative optional modules which may vary year-on-year.
Behavioural Finance

Study financial markets using models that are less narrow than those based on von Neumann-Morgenstern expected utility theory and arbitrage assumptions.

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Statistical Learning & Big Data

Cover a range of theories in statistical learning and big data and big model related issues and solutions.

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Advanced Trading Strategies

Get an introduction to three advanced topics in Mathematical Finance, computing and explaining key variables, applying appropriate techniques, and analysing and comparing different modelling approaches between the three. 

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Partial Differential Equations in Finance

Gain a theoretical and practical understanding of partial differential equations.

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Advanced Risk Management

Develop the conceptual understanding and mathematical skill required to address risk analysis and management problems in realistic scenarios.

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Our online platform

Our unique online learning platform my.wbs will act as the hub for your learning experience, hosting all of your teaching materials, key information from your CareersPlus and Programme Teams, and our online classroom: wbsLive.

Your learning experience

Teaching and assessment will vary to fit the subject being studied. Our faculty have designed your learning experience to fit the subject being studied, with a variety of teaching and assessment styles.

Your teaching will take the form of lectures, webinars, and computer lab sessions, supported, where relevant, by guest speakers from industry to balance theory and practice. Lectures introduce key theories, concepts, and economic models. In classes you will solve financial problems and numerical exercises, analyse case studies, and make presentations of research published in academic journals.

Modules are taught by staff from WBS, Warwick's Department of Statistics, and the Mathematics Institute through a combination of lectures, classes, and computer lab sessions. A one-week induction module will ensure you have the mathematical and statistical prerequisites for the course.

Your learning will be assessed by a mix of exams, group work, project work, and tests. Group and project work will enable you to collaborate and gain new perspectives from your international cohort, preparing you for working in teams within a global work environment. 

Lab work will give you hands-on experience of using software to perform finance-related calculations, conduct realistic simulations and write code.

Learning facilities

You will have access to our outstanding learning facilities, including a Postgraduate-only Learning Space and IT suite, as well as all other University facilities. Studying on a Finance-based MSc also means that you gain access to our Bloomberg terminals, Eikon terminals, and financial data via the Wharton Research Data Services.  

Learn from the practitioners

Guest lectures by practitioners from the quantitative finance industry will give an applied context to your course, showcasing how the course prepares you for a variety of graduate destinations, and giving valuable networking opportunities. 

External companies also provide a selection of dissertation projects for our students, giving the opportunity for you to apply your knowledge in a real corporate setting.