Triangle

Course overview

The course teaches advanced financial mathematics combined with computational techniques. You'll learn mathematical techniques and skills that are used across the financial sector to quantify and hedge risk. Practical experience of using computational and statistical software will develop your skills in mathematical modelling.

This MSc at Nottingham can provide many career opportunities in sectors including:

  • investment and commercial banking
  • quantitative and actuarial analysis
  • hedge fund management
  • research in academia and industry

Course structure

The programme includes teaching expertise from the School of Mathematical Sciences and the School of Economics. You will study core modules including:

  • Financial Mathematics
  • Computational Applied Mathematics,
  • Scientific Computing and C++

Choose optional modules such as Statistical Machine Learning or Economics of Corporate Finance to suit your interests. Expert teaching staff will support you and guide you through your written dissertation.

A highlight of the course is our partnerships with leading industry and academic experts. The panel includes individuals from Credit Suisse, Oxford University and other organisations, to ensure the degree remains relevant and up to date with changes in quantitative finance.

Representatives from companies such as Bloomberg, Capital One, HSBC and PwC also provide guest lectures during the year.

Research Excellence Framework 2021

  • 97% of our research outputs are rated as 'world-leading' or 'internationally excellent'
  • The school was placed in the top 3 for quality of research environment across all mathematical sciences units in the UK
  • 100% of the impact from the school is rated as either ‘world-leading’ or ‘internationally excellent’

This is highlighted in our commitment to attracting bright minds and inspiring academics to conduct mathematical research throughout the department. 

Why choose this course?

Advisory board

consisting of leading financial industry and academic experts to ensure our MSc is topical

Guest lectures

network with experts from Capital One, Bloomberg, HSBC and PwC

Top 3 in UK

for research environment

Research Excellence Framework 2021

Scholarships available

to help fund your postgraduate course

Course content

The course is made up of 120 credits of taught modules and a 60 credit financial mathematics dissertation.

On arrival to Nottingham, you will be given the opportunity to select your preferred stream from:

Optional stream 1

Mathematics, Statistics and Computing

It complements the core modules with applied mathematics and statistics knowledge.

Optional stream 2A 

Econometrics

The focus of this stream is on econometrics. It provides advanced knowledge on mathematical and statistical methods applied in economics and finance. Some modules are taught by the School of Economics.

Optional stream 2B

Microeconomics

The focus of this stream is on microeconomics, with Economics and Finance modules offered by the School of Economics. They are complemented by modules providing advanced knowledge on mathematical methods offered by the School of Mathematical Sciences.

Optional stream 2C

Big data economics

This stream is focused towards the growth in big data and being able to interpret the outputs from an economic perspective.

Modules

Core modules

Advanced Financial Mathematics

In this module you will cover three major topics: continuous time modelling for equity derivatives pricing, pricing interest rate derivatives and credit risk modelling. These will be underpinned by the theory of stochastic processes and stochastic differential equations.

You will gain experience of a topic of considerable contemporary importance, both in research and in terms of how it is applied. You will undertake a group project which will involve independent reading, computer simulations and a written report.

Computational Applied Mathematics

During this module four major topics for the computational solution of problems in applied mathematics are considered.

  • approximation theory
  • numerical solution of nonlinear problems
  • numerical solution of ODEs
  • numerical solution of PDEs.

The focus is on formulating and understanding computational techniques with illustrations on elementary models from a variety of scientific applications. Specific contents include: approximation theory, multivariate polynomial approximation, Gauss quadrature, splines, trigonometric polynomials, DFTs, FFTs; numerical solution of (systems of) nonlinear equations; numerical differentiation and numerical solution of ODEs; introduction to PDEs and finite difference methods including error analysis.

Financial mathematics

This module introduces the main concepts of financial mathematics, such as pricing and hedging of financial instruments (forwards, futures, options, swaps) with a focus on discrete models. You will develop your knowledge of probability and stochastic calculus. 

The first part of the course introduces the no-arbitrage pricing principle and financial instruments such as forward and futures contracts, bonds and swaps, and options.

The second part of the course considers the pricing and hedging of options and discrete-time discrete-space stochastic processes.

The final part of the module focuses on the Black-Scholes formula for pricing European options and also introduces the Wiener process, Ito integrals and stochastic differential equations.

Scientific Computing and C++

This module provides an introduction to the programming language C++ with a strong emphasis on scientific computing applications. You will study C++ Language: basic types and control structures, program design and implementation, program comprehension and modification, program testing and documentation; pointers, functions, and reference variables. You will also cover classes, inheritance and derived classes; templates, applications, computer roundoff and its effect on the design of algorithms; polynomial interpolations and numerical integration. You will also gain an understanding of computational linear algebra, including direct and iterative methods.

Financial and Computational Mathematics Dissertation

You will carry out a substantial investigation on a topic in financial mathematics and related subjects. The study will be largely self-directed, with oversight and input provided where necessary by a supervisor from the School of Mathematical Sciences. The topic will be chosen from a list of potential projects provided by the school.

The projects will usually contain three components: a finance-related part; mathematical and/or statistical methods and analysis; element of computing either using existing packages or developing new code to simulate and analyse appropriate mathematical models. The balance of the three components will depend on the nature of a particular project.

A training session covering the oral presentation assessment criteria and some elements of good practice will also be included.

Optional stream 1: Mathematics, Statistics and Computing

Optimization

In this module a variety of techniques and areas of mathematical optimization will be covered. You will study topics such as lagrangian methods for optimization, linear programming including the simplex algorithm, dynamic programming both deterministic control problems and stochastic problems. You will also cover network and graph algorithms.

During the module you will gain a rigorous mathematical background and develop the techniques for application through computational examples.

Statistical Foundations

In this module, the fundamental principles and techniques underlying modern statistical and data analysis will be introduced. You will gain experience in using a statistical package and interpreting its output. The course will cover a 'common core' consisting of:

  • statistical concepts and methods
  • linear models
  • probability techniques
  • Markov chains
Statistical Machine Learning

This module is a topic at the interface between statistics and computer science, concerning models that can adapt to, and make predictions based on, data. This module builds on the principles of statistical inference and linear regression to introduce a variety of methods of clustering, dimension reduction, regression and classification.

Much of the focus is on the bias-variance trade-off and on methods to measure and compensate for overfitting. The learning approach is hands-on; you will be using R extensively in studying contemporary statistical machine learning methods, and in applying them to tackle challenging real-world applications.

Time Series and Forecasting

This module will provide a general introduction to the analysis of data that arise sequentially in time. Several commonly occurring models will be discussed and their properties derived. Methods for model identification for real-time series data will be considered. Techniques for estimating the parameters of a model, assessing its fit and forecasting future values will be developed. You will gain experience of using a statistical package and interpreting its output.

Optional stream 2A: Econometrics

The focus of this stream is on econometrics. It provides advanced knowledge on mathematical and statistical methods applied in economics and finance. Some modules are taught by the School of Economics.

Econometric Theory

Core techniques of econometric theory, including detailed analysis of the multiple linear regression model; large sample theory; asymptotic testing procedures; non-linear techniques; and mis-specification testing will be covered.

Financial and Macro Econometrics

The module extends the coverage of advanced econometric modelling techniques and considers their application through the study of selected topics in finance and macroeconomics, developing familiarity and critical awareness of empirical research in these areas. You will study techniques for the analysis of stationary ARMA processes, Vector Autoregressions (VARs), linear regression models, linear systems of simultaneous equations, cointegration, long-run structural VARs, forecasting, and models of changing volatility. The selected topics include the econometric analysis of business cycle fluctuations, wage, price and (un)employment determination, portfolio choice and stock market returns.

Game Theory

Game theory contains many branches of mathematics (and computing); the emphasis here is primarily algorithmic. The course starts with an investigation into normal-form games, including strategic dominance, Nash equilibria, and the Prisoner's Dilemma. We look at tree-searching, including alpha-beta pruning, the killer heuristic and its relatives. It then turns to the mathematical theory of games; exploring the connection between numbers and games, including Sprague-Grundy theory and the reduction of impartial games to Nim.

Mathematics for Engineering Management

This module examines and classifies various (non-statistical) management and operational research problems and their formulation and techniques for solution.

Techniques introduced and used concentrate on operations research problems such as linear programming (LP), dynamic programming and nonlinear programming problems. You will gain an understanding of graphical solution to simple LP problems along with the use of simplex method for the numerical/computational solution of LP problems.

Time Series Econometrics

The module covers fundamental properties of time series and various classes of stochastic processes. Issues in estimation and forecasting of time series models; concepts of contemporary interest to time series econometricians are also covered.

Optional stream 2B: Microeconomics

The focus of this stream is on microeconomics, with economics and finance modules offered by the School of Economics. They are complemented by modules providing advanced knowledge on mathematical methods offered by the School of Mathematical Sciences.

Economics of Corporate Finance

This module offers you an introduction to the economics of corporate finance. It is designed to provide you with the basic theoretical background in this area that is necessary for any applied work. Emphasis is placed on the analysis of simple models and their applications.

The module covers a variety of topics with substantial time devoted for covering issues directly related to the financial needs of firms, such as capital structure, credit rationing and corporate governance.

The module also examines the role of financial intermediaries analyzing bank failures and, consequently, the scope for banking regulations. The last part of the module looks closely at the relationship between the financial sector and the real economy thus offering the background for any applied work related to the link between financial development and economic fluctuations.

Game Theory

Game theory contains many branches of mathematics (and computing); the emphasis here is primarily algorithmic. The course starts with an investigation into normal-form games, including strategic dominance, Nash equilibria, and the Prisoner's Dilemma. We look at tree-searching, including alpha-beta pruning, the killer heuristic and its relatives. It then turns to the mathematical theory of games; exploring the connection between numbers and games, including Sprague-Grundy theory and the reduction of impartial games to Nim.

Mathematics for Engineering Management

This module examines and classifies various (non-statistical) management and operational research problems and their formulation and techniques for solution.

Techniques introduced and used concentrate on operations research problems such as linear programming (LP), dynamic programming and nonlinear programming problems. You will gain an understanding of graphical solution to simple LP problems along with the use of simplex method for the numerical/computational solution of LP problems.

Microeconomics: Consumer and Firm Behaviour

This module covers foundations of consumer theory, decision-making under risk, elements of the theory of imperfect competition, and incomplete information in markets.

Macroeconomics: Economic Cycles, Frictions and Policy

The module lays the foundations of macroeconomic models with the use of very stylized one and two-period models in which consumers, firms and governments interact in the goods and factor markets. You will also study the determinants of long-run economic growth. The overarching question that we will consider is: why are some countries so much richer than others?

We will study several mechanisms that can potentially account for these differences, namely, capital accumulation, knowledge production and institutions. Finally, we will embed the consumption and labour supply decision studied at the beginning of the semester into a dynamic stochastic general equilibrium model of the macroeconomy. The standard real business cycle (RBC) model developed by Kydland and Prescott will be considered alongside the investment decision of firms, and you will spend some time studying the search and matching model of unemployment of Mortensen and Pissarides.

Monetary Theory and Practice

This module covers monetary aspects of advanced macroeconomics. It focuses on the theory and practice of central banking, monetary policy and control. It covers concepts such as time inconsistency, the problem of inflation bias with solutions, credibility, transparency and accountability of monetary institutions. You will also cover inflation targeting and price stability, the choice of instruments for monetary policy and their control, and finally monetary transmission. You will combine some theory with evidence and practice. Students that take this module typically aim to work in central banks, financial institutions or government.

Optional stream 2C: Big data economics

This stream is focused towards the growth in big data and being able to interpret the outputs from an economic perspective. Some modules are taught by the School of Economics.

Big Data Economics

This module will focus on advanced Big Data methods and their applications in various economics problems. Topics of the module include:

  • nonlinear models
  • tree-based models
  • support vector machines
  • unsupervised learning and applications in international trade
  • household finance
  • macro forecasting
  • labor economics
  • text analysis
Game Theory

Game theory contains many branches of mathematics (and computing); the emphasis here is primarily algorithmic. The course starts with an investigation into normal-form games, including strategic dominance, Nash equilibria, and the Prisoner's Dilemma. We look at tree-searching, including alpha-beta pruning, the killer heuristic and its relatives. It then turns to the mathematical theory of games; exploring the connection between numbers and games, including Sprague-Grundy theory and the reduction of impartial games to Nim.

Machine Learning for Economics

This module is intended as an introduction of the methodology and implementation of machine learning methods used widely in economic analysis; the module provides an introduction to the analysis of large datasets, with more up to date Big Data methods introduced later as natural developments. Topics will include:

  • an introduction to statistical computing in R
  • generating random variables and random processes
  • Monte Carlo integration and variance reduction; high performance computing
  • multivariate linear regression
  • classification
  • resampling methods
  • linear model selection and regularization
Mathematics for Engineering Management

This module examines and classifies various (non-statistical) management and operational research problems and their formulation and techniques for solution.

Techniques introduced and used concentrate on operations research problems such as linear programming (LP), dynamic programming and nonlinear programming problems. You will gain an understanding of graphical solution to simple LP problems along with the use of simplex method for the numerical/computational solution of LP problems.

The above is a sample of the typical modules we offer but is not intended to be construed and/or relied upon as a definitive list of the modules that will be available in any given year. Modules (including methods of assessment) may change or be updated, or modules may be cancelled, over the duration of the course due to a number of reasons such as curriculum developments or staffing changes. Please refer to the module catalogue for information on available modules. This content was last updated on Thursday 08 September 2022.

Learning and assessment

How you will learn

  • Lectures
  • Problem classes
  • Independent study
  • Supervision
  • Presentation
  • Computer workshops
  • Research project
  • Group study

You will broaden and deepen your knowledge of mathematical ideas and techniques using a wide variety of different methods of study.             

How you will be assessed

  • Examinations
  • Coursework
  • Reports
  • Programming tasks

The mathematics modules of the course are assessed by:

  • Examinations
  • Coursework
  • Written reports
  • Oral reports
  • Programming tasks

The economics modules of the course are assessed by:

  • Examinations
  • Coursework

You will be awarded the Master of Science Degree provided you have successfully completed the taught stage by achieving a weighted average mark of at least 50% with no more than 40 credits below 50% and no more than 20 credits below 40%.

You must also achieve a mark of at least 50% in the dissertation.

Contact time and study hours

The number of formal contact hours varies depending on the optional modules you are studying. As a guide, in the Autumn and Spring semesters you will typically spend around 15 hours per week (between Monday and Friday) in lectures.

You will work on your research project between June and September, usually based at the University.

Teaching is provided by academic staff within the School of Mathematical Sciences and the School of Economics. Modules are typically delivered by Professors, Associate and Assistant Professors. Additional support in small group and practical classes may involve PhD students and post-doctoral researchers.

The majority of your lecturers and tutors will be based within the Mathematical Sciences building. This means if you need to get in touch with them during office hours, they can be contacted easily as they are close by.

Class sizes are typically no more than 20 students.

Entry requirements

All candidates are considered on an individual basis and we accept a broad range of qualifications. The entrance requirements below apply to 2023 entry.

Undergraduate degree2:1 in mathematics, physics or engineering
Portfolio

Applicants should have a solid background in mathematics including calculus, linear algebra, ordinary differential equations and probability and statistics at degree level.

In exceptional cases, applicants holding a 2.2 with substantial mathematical content may be considered.

Applying

Our step-by-step guide covers everything you need to know about applying.

How to apply

Fees

UK fees are set in line with the national UKRI maximum fee limit. We expect fees for 2023 entry to be confirmed in August 2022.

Additional information for international students

If you are a student from the EU, EEA or Switzerland, you may be asked to complete a fee status questionnaire and your answers will be assessed using guidance issued by the UK Council for International Student Affairs (UKCISA) .

These fees are for full-time study. If you are studying part-time, you will be charged a proportion of this fee each year (subject to inflation).

Additional costs

All students will need at least one device to approve security access requests via Multi-Factor Authentication (MFA). We also recommend students have a suitable laptop to work both on and off-campus. For more information, please check the equipment advice.

As a student on this course, we do not anticipate any extra significant costs, alongside your tuition fees and living expenses.

Printing

Due to our commitment to sustainability, we don’t print lecture notes but these are available digitally. You are welcome to buy print credits if you need them. 

Books

You should be able to access most of the books you’ll need through our libraries, though you may wish to purchase your own copies which you would need to factor into your budget.

Computers

Personal laptops are not compulsory as we have computer labs that are open 24 hours a day but you may want to consider one if you wish to work at home.

Funding

School scholarships for UoN alumni

We invite our alumni to continue with us for masters study. 10% alumni scholarships may be offered to University of Nottingham graduates (both UK/EU and International) who have studied at the UK campus, for 2023/2024 entry. 

International students

In 2023/24 a limited number of scholarships of up to £2,000 will be awarded by the School of Mathematical Sciences to international students which will allow you to also apply for other scholarships. 

There are many ways to fund your postgraduate course, from scholarships to government loans.

We also offer a range of international masters scholarships for high-achieving international scholars who can put their Nottingham degree to great use in their careers.

Check our guide to find out more about funding your postgraduate degree.

Postgraduate funding

Careers

We offer individual careers support for all postgraduate students.

Expert staff can help you research career options and job vacancies, build your CV or résumé, develop your interview skills and meet employers.

Each year 1,100 employers advertise graduate jobs and internships through our online vacancy service. We host regular careers fairs, including specialist fairs for different sectors.

International students who complete an eligible degree programme in the UK on a student visa can apply to stay and work in the UK after their course under the Graduate immigration route. Eligible courses at the University of Nottingham include bachelors, masters and research degrees, and PGCE courses.

Graduate destinations

The ability to think logically and critically combined with your problem-solving expertise gained on the course will prepare you for future employment.

Our graduates have gone on to work as business analysts, data scientists and trading analysts.

They work for organisations across the UK and worldwide:

The course also provides suitable training for a PhD in areas such as financial mathematics, quantitative finance and applied mathematics.

Career progression

84.8% of postgraduate taught students from the School of Mathematical Sciences secured graduate level employment or further study within 15 months of graduation. The average annual salary for these graduates was £30,374.*

*HESA Graduate Outcomes 2019/20 data published in 2022. The Graduate Outcomes % is derived using The Guardian University Guide methodology. The average annual salary is based on data from graduates who completed a full-time postgraduate taught degree with home fee status and are working full-time within the UK.

To continuously keep the course in touch with changes in the area of quantitative finance, we work with an advisory board comprising of experts from financial firms and academia including:

  • Capital One
  • Oxford University
  • Gorilla Science, Florida
  • Bloomberg
  • Credit Suisse
  • Financial Conduct Authority
Two masters graduates proudly holding their certificates
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Reuben O'Dea

Course director

Related courses

This content was last updated on Thursday 08 September 2022. Every effort has been made to ensure that this information is accurate, but changes are likely to occur given the interval between the date of publishing and course start date. It is therefore very important to check this website for any updates before you apply.