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  • DeadlineStudy Details:

    MSc 1 year full-time

Course Description

Challenge yourself with a high-level mix of mathematic and finance disciplines

This mathematically rigorous course is unique in providing training from three top departments at Warwick:  Statistics, Mathematics and Warwick Business School. It enables you to develop and apply the quantitative skills in machine learning, computational statistics and mathematical finance used in the financial markets and the finance industry. 

Build on your strong mathematical background to gain both a deep theoretical and conceptual knowledge of finance, together with the requisite high-level probability, statistics and mathematics, to enable you to undertake advanced quantitative modelling. Lab work will give you hands-on experience of using software packages for simulations and time series analysis, as well as learning programming for quantitative finance in three core languages: Python, C++ and R.

Our departments benefit from excellent links to key financial institutions and employers, and this course has benefited from industry recommendations.

Please note that this course was previously named MSc Financial Mathematics.

Entry Requirements

Our MSc Mathematical Finance is a highly specialised and technical programme. To succeed on this course, you will need strong ability in Mathematics and Finance, and some experience in Statistics or Econometrics, evidenced by a high 2.1 or first in Mathematics, Statistics, Physics or another relevant quantitatively-focused undergraduate degree.  A clear emphasis in your choice of undergraduate electives on the aforementioned areas will strengthen your application.

When assessing your academic record, we take into account your grade average, and the position of the institution where you studied your qualification.

You must have, or be expecting to obtain, at least a high 2.1 degree at Undergraduate level from a UK university, or overseas equivalent from a top-performing University.

While you will, throughout the course, develop all necessary advanced skills, confident foundations in mathematics (in particular sound knowledge of Probability Theory, Linear Algebra and Differential Calculus) are necessary to build on.

Given the fast pace of the course, it is strongly recommended that you undertake preliminary reading before the start of the course if you have little or no background in areas such as Measure-Theoretic Probability, Statistics, and Financial Economics. Before you join the course, we will make reading recommendations and other materials available via our dedicated pre-arrival pages.

  • A first or high 2.1 achieved in mathematics and statistics at Undergraduate level
  • We do not require a GMAT or GRE score but a well-balanced score (700+) may strengthen your application.

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Fees

For fees and funding information please see our website

Programme Funding

WBS Scholarships

We are investing in the future by enabling talented students to receive a top-quality business education.

We have £1million+ of WBS Scholarships available for students applying to study on our MSc postgraduate courses.

Scholarships awards range from 10% of your tuition fee to a maximum of 50% for exceptional candidates. They are highly competitive and awarded on an ongoing basis, so applicants are strongly encouraged to apply early.

We focus on attracting intellectually curious individuals who strive for excellence in everything they do. We welcome talented candidates from across the globe who are passionate about achieving their full potential, both academically and professionally.

Student Destinations

What do our graduates do? 

  • Junior Quantitative Analyst
  • Director of Trading
  • Market Risk Analyst
  • Senior Investment Analyst
  • Quantitative Strategy
  • Head of Fixed Income options IT
  • Valuation Controller
  • Fixed Income ETF Trader
  • Quantitative Financial Analyst

Module Details

Compulsory Modules

  • Programming for Quantitative Finance
  • Stochastic Calculus for Finance
  • Financial Statistics
  • Simulation & Machine Learning for Finance
  • Asset Pricing & Risk
  • Financial Econometrics
  • Applications of Stochastic Calculus in Finance
  • Dissertation

Optional Modules

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

  • Behavioural Finance
  • Statistical Learning & Big Data
  • Advanced Trading Strategies
  • Partial Differential Equations in Finance

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