Postgraduate study
This course focuses on the mathematical modelling and computational techniques used in the financial industry, as well as the required background in finance.
MSc Financial and Computational Mathematics
1 year full-time
Entry requirements
2:1 (or international equivalent) in mathematics, physics or engineering; a strong mathematics background is essential
Other requirements
In exceptional cases, applicants holding a 2.2 (or international equivalent) with substantial mathematical content may be considered
6.5 (no less than 6.0 in any element)

If these grades are not met, English preparatory courses may be available
Start date
UK/EU fees
£11,475 - Terms apply
International fees
£22,815 - Terms apply
University Park Campus



  • we have our own advisory board consisting of leading experts from the financial industry and academia to ensure the course stays relevant
  • be taught by experts from the School of Mathematical Sciences and the School of Economics
  • benefit from an industry-engaged course, with regular talks and workshops from industry partners and academics from the University of Nottingham
  • choose optional modules that reflect your future career ambitions

Financial mathematics is a branch of mathematics where advanced mathematical and statistical methods are developed for and applied to financial markets and financial management. Its main aims are to quantify and hedge risks in the financial marketplace.

Effective computational methods are crucial for the successful use of mathematical modelling in finance. This course is designed to reflect this combination of knowledge and skills so that graduates are well equipped to enter the competitive job markets of quantitative finance and related fields. Optional modules also equip graduates with foundations of data analytics.

Applicants should have a solid background in mathematics including calculus, linear algebra, ordinary differential equations and basic techniques used in probability and statistics.

I liked the variety of topics covered on this course. Financial maths, C++ and statistical machine learning are three seemingly separate topics but have all been applicable to my career.
Dan Nicholls, graduate and has worked as a Data Scientist and Software Developer for Kx Systems

Research in the School of Mathematical Sciences

This course is linked to the research undertook in the school. The research groups most closely related are:

We are ranked top 10 in the UK for research power in our area of assessment in the Research Excellence Framework 2014. 

Meet your course director

Professor Michael Tretyakov
Course Director

I have extensive world-class expertise in stochastic numerical analysis and, in particular, wrote a research monograph on this subject (joint with G.N. Milstein, Springer 2004). These numerical methods (especially, the Monte Carlo technique) are widely used in the financial industry. I also conduct high quality research in uncertainty quantification, financial mathematics and stochastic modelling. 

Based on my teaching experience, I wrote an introductory textbook on financial mathematics (ICP,2013), which is used for teaching the core Financial Mathematics module here and elsewhere.

I currently teach the Advanced Financial Mathematics module which is also core for this course, and I supervise a few MSc dissertations every summer. In developing, supporting and teaching on this course, I follow the principle that a specialised MSc should equip graduates with skills and knowledge required to be competitive on the highly demanding job market.

As Course Director, my duty is to insure the quality of the degree and its smooth running. I’m also involved in organising extracurricular activities and providing career advice for MSc students. 

Academic English preparation and support

If you require additional support to take your language skills to the required level, you may be able to attend a presessional course at the Centre for English Language Education, which is accredited by the British Council for the teaching of English in the UK.

Students who successfully complete the  presessional course to the required level can progress to postgraduate study without retaking IELTS or equivalent. You could be eligible for a  joint offer, which means you will only need to apply for your visa once. 


Full course details

I found it useful to learn about coding and modelling on the course. I enjoyed the financial modules and being able to see them in the workplace.
Lizzie Nicholson, graduate and now working for Ford Motor Companies as a Funding Analyst 

Advisory board and expert support

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

  • Darren Carlile (Capital One)
  • Mike Giles (Oxford University)
  • Pat Hagan (Gorilla Science, Florida)
  • Phil Harrold (ex PwC)
  • Phil McCabe (Bloomberg)
  • Tim Sharp (Credit Suisse)

The programme also benefits from expert support by:

  • Stefan Hunt (Financial Conduct Authority)

The financial industry operates in a rapidly changing environment and to keep the degree in line with changes happening in the industry, we involve senior experts from financial firms and academia in various aspects of the MSc. For instance, the course director sends an annual report to the board members about the MSc performance and changes, and they will be invited to comment. Members of the board and experts from financial firms are invited to give guest lectures.

Extracurricular activities

To unlock your full potential, we run a number of extracurricular activities, which are an integral part of the course. Activities include talks and workshops by our industrial partners as well as in-house training and talks by leading academics in the school.

We also offer two additional free modules via the Nottingham Advantage Award that are in collaboration with Capital One and Experian. 

Find out more

Teaching and assessment

MSc Financial and Computational Mathematics is a one year full-time degree.

Classes are in the form of teaching/learning sessions, including computer practical sessions.

As future graduates, you will gain experience of the types of problems encountered by academics and quantitative practitioners, both via taught courses and project work in an individual and group environment. Written and oral presentations will also be undertaken at various stages of the course.

The course also includes a substantial individual project, which develops your ability to engage in independent learning. The project will form the basis of your written dissertations.

Other skills that you should develop include the ability to think logically and critically, problem-solving expertise, competent use of relevant software, and effective communication of results.

The lecturers are very enthusiastic to offer further explanations. I would adivse new students to take advantage of the University's relationship with employers.
Michael Kennedy, graduate and now at a fixed income options trading desk of a proprietary firm in London

Prerequisite information

This course is designed for students with a first degree in mathematics or a related subject with substantial mathematical content (eg engineering and physics, computer science and economics). If you’re taking this course, you won’t need a background in finance, just enthusiasm and a willingness to learn the subject.


The following book gives an indication of the level of mathematics required. A basic understanding of the content of chapters 1-3 and 12-16 would be advisable.

  • All the Mathematics You Missed (But Need to Know for Graduate School), Thomas A Garrity (CUP)

Scientific computing

  • An Introduction to Numerical Analysis, Endre Suli and David Mayers (CUP).

You should be able to understand, with some work, chapters 1-2, 6-7 and 11-12. Alternatively, you can use the textbook Numerical Mathematics by Alfio Quarteroni, Riccardo Sacco, Fausto Saleri published by Springer, where you can examine the basics of chapters 1-3, 6, 8-11 and 13.


Probability and statistics

Applicants should be familiar with elementary probability and statistics. The following are suggested as basic texts for revision purposes:

  • Introductory Probability and Statistical Applications, P.L. Meyer
  • Probability (first two chapters), A.N. Shirayaev
  • A First Course in Probability, S.M. Ross
  • Probability and Math Statistics, L.D. Taylor


We do not require any prior knowledge of C++. You may however wish to look through any introductory book on C++ such as:

  • Guide to Scientific Computing in C++, J.P. Francis and J. Whiteley, J.R. Hubbard
  • Schaum's Outline of Programming with C++,  J.R. Hubbard
  • Schaum's Outline of Fundamentals of Computing with C++ (or the online tutorial)

Financial mathematics

We do not require any prior knowledge of financial mathematics or finance but if you would like to undertake some preliminary reading, you can refer to:

  • Stochastic Calculus for Finance, S.E. Shreve (I. Springer, 2004)
  • Introductory Course on Financial Mathematics, M.V. Tretyakov (ICP, 2013)




The structure of the MSc is modular, with individual modules having either 20, 15 or 10 credits. One credit represents 10 hours of student work, meaning that a 20 credit module represents 200 hours of study including formal teaching, independent study, revision, and the preparation of assessments.

The MSc degree requires the successful completion of 180 credits, 120 of which are taught modules, and 60 credits of a financial mathematics dissertation.


Advanced financial mathematics

This module builds upon the concepts from finance and your knowledge of probability and stochastic processes introduced in the Financial Mathematics module. It has three major topics: continuous time modelling for equity derivatives pricing, pricing interest rate (fixed-income) derivatives, and credit risk modelling, which are underpinned by the theory of stochastic processes and stochastic differential equations. 
Students will gain experience of a topic of considerable contemporary importance, both in research and in applications. A group project will be undertaken which will involve independent reading, computer simulations, and a written report. You will acquire knowledge and skills relevant to the mathematical modelling widely used in the financial industry for financial risk management.


Computational applied mathematics

Four major topics for the computational solution of problems in applied mathematics are considered in this course:

  • approximate 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:

  • approximations theory, multivariate polynomial approximation
  • numerical differentiation and numerical solution of ODEs
  • introduction to PDEs, finite difference methods and FFT (Fast Fourier Tranforms) for PDEs
  • numerical solution of (systems of) nonlinear equations.

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. It also develops your knowledge of probability and stochastic calculus. 

  • the first part of the course introduces 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.

Financial and computational mathematics dissertation

A substantial investigation will be carried out 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 of Mathematical Sciences.

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.

The topics included:

  • feature generation project: Statistical techniques and the probability of default (with a collaborating financial firm)
  • the optimal portfolio rebalancing strategy with trading costs
  • a modified SABR model for pricing FX options (with a collaborating financial firm)
  • jumps in high-frequency financial data
  • extension of the skew normal distribution and applications in financial options pricing (with a collaborating financial firm)
  • credit value adjustment for interest rate swaps
  • deciphering your credit score (with a collaborating financial firm)

Scientific computing and C++

This course presents an introduction to the programming language C++, with a strong emphasis on scientific computing applications. A detailed list of key topics covered by this module is given below.

C++ Language:

  • basic types and control structures, program design and implementation, program comprehension and modification, program testing and documentation. 
  • pointers, functions, and reference variables.
  • classes, inheritance and derived classes.
  • templates.


  • computer roundoff and its effect on the design of algorithms.
  • polynomial interpolations. 
  • numerical integration.
  • computational linear algebra, including direct and iterative methods.

In addition, a training session covering the oral presentation assessment criteria and some elements of good practice will be included as part of this module.


Optional streams

Students have the option to choose a combination of optional modules from three different streams, which allows you to customise your masters based on your interests and needs. I opted for the economics modules, which are offered in conjunction with the School of Economics.
Elisabetta Di Lauro, graduate and now at Citibank


Students must also take 40 credits restricted to one of the three streams below:

Stream one – Mathematics/Statistics and Computing


In this module a variety of techniques and areas of mathematical optimisation will be covered. The module will contain sections covering the following topics:

  • Lagrangian methods for optimisation, overview of uses in a variety of applications some of which appear in later sections
  • Linear Programming including the Simplex Algorithm
  • Dynamic Programming both deterministic control problems and stochastic problems
  • Network and Graph algorithms

The module not only will tackle the problems from a rigorous mathematical background, but also then develop the techniques for application through computational examples.


Statistical foundations

In this course the fundamental principles and techniques underlying modern statistical and data analysis will be introduced.

The course will cover a 'common core' consisting of:

  • statistical concepts and methods;
  • linear models;
  • probability techniques;
  • Markov chains.

Students will gain experience of using a statistical package and interpreting its output. The common core material will be covered primarily at the beginning of the semester.


Time series and forecasting

This course 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 described. Techniques for estimating the parameters of a model, assessing its fit and forecasting future values will be developed.

Students will gain experience of using a statistical package and interpreting its output.

The course will cover:

  • concepts of stationary and non-stationary time-series;
  • philosophy of model building in the context of time series analysis;
  • simple time series models and their properties;
  • the model identification process;
  • estimation of parameters;
  • assessing the goodness of fit;
  • methods for forecasting;
  • use of a statistical package.

Statistical machine learning

Machine Learning is a topic at the interface between statistics and computer science that concerns models that can adapt to and make predictions based on data.

This course builds on principles of statistical inference and linear regression to introduce a variety of methods of regression and classification, trade-off, and on methods to measure and compensate for overfitting.

The learning approach is hands on, with students using R to tackle challenging real world machine learning problems.


The modules Time Series and Forecasting and Statistical Machine Learning provide the graduates with solid statistical background required in data analytics.

Stream two A

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
  • mis-specification testing

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. 

It covers 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 Prisoners Dilemma. We look at tree-searching, including alpha-beta pruning, the killer heuristic and its relatives.

It then turns to 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

The course 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.

Topics include:

  • understanding of graphical solution to simple LP problems.
  • use of Simplex method for numerical / computational solution of LP problems.
  • evaluation of effects of parameter changes in LP problems.
  • optimisation of a multi-stage problem using dynamic programming techniques.
  • search techniques to finding an optimum of a nonlinear function of several variables.

Time series econometrics

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.


Stream two B

Economics of corporate finance

This module offers an introduction to the economics of Corporate Finance. It is designed to provide students 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 analysing 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 Prisoners Dilemma. We look at tree-searching, including alpha-beta pruning, the killer heuristic and its relatives.

It then turns to 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

The course 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. Topics include:

  • understanding of graphical solution to simple LP problems.
  • use of Simplex method for numerical / computational solution of LP problems.
  • evaluation of effects of parameter changes in LP problems.
  • optimisation of a multi-stage problem using dynamic programming techniques.
  • search techniques to finding an optimum of a nonlinear function of several variables.

Macroeconomics: economic cycles, frictions and policy

We start by laying the foundations of macroeconomic models with the use of very stylised one and two-period models in which consumers, firms and governments interact in the goods and factor markets. We will discuss how to solve macroeconomic models in the computer with the aid of these simple economies.

We next move to study the determinants of long-run economic growth. The overarching question that we will study 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. This section does not seek to be an exhaustive survey of models of economic growth; it intends to highlight the mechanisms at play in a broad class of models. We will also place a strong emphasis on how to take the predictions derived from these models to the data.

In the last section of the module, we turn our attention towards the ‘short run’. 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. We will study the investment decision of firms, and close the module by studying the search and matching model of unemployment of Mortensen and Pissarides.


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.


Monetary theory and practice

This module covers monetary aspects of advanced macroeconomics and is suitable for students of mainstream economics, finance and international economics. 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. Inflation targeting and price stability, the choice of instruments for monetary policy and their control, and finally monetary transmission. It combines some theory with evidence and practice. Students that take this module typically aim to work in central banks, financial institutions or government.



The above is a sample of the typical modules that 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. Due to the passage of time between commencement of the course and subsequent years of the course, modules may change due to developments in the curriculum and information is provided for indicative purposes only.


Fees and funding

UK/EU students 

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

Graduate school

The Graduate School provides more information on internal and external sources of postgraduate funding.

School scholarships for UoN UK 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 2019/2020 entry.

International students


In 2019/20 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.

Government loans for masters courses

The Government offers postgraduate student loans for students studying a taught or research masters course. Applicants must ordinarily live in England or the EU. Student loans are also available for students from Wales, Northern Ireland and Scotland.

International and EU students

Masters scholarships are available for international students from a wide variety of countries and areas of study. You must already have an offer to study at Nottingham to apply. Please note closing dates to ensure your course application is submitted in good time.

Information and advice on funding your degree, living costs and working while you study is available on our website, as well as country-specific resources.


Careers and professional development

I am now working in Shenzhen, China, as a quantitative researcher. My job deals with investment strategy development, derivative pricing and data analysis.
Ruizhi Liu, graduate of MSc Financial and Computational Mathematics

This course offers a solid grounding in financial mathematics and will prepare you for quantitative roles in banks and other financial institutions dealing with risk analysis and management.

The course also provides training suitable for admission on PhD programmes in financial mathematics and quantitative finance. You will gain experience of the type of problems encountered by academic and qualitative practitioners, both via taught courses and project work on an individual and group basis. 

Average starting salary and career progression

In 2017, 100% of postgraduates in the school who were available for employment had secured work or further study within six months of graduation. The average starting salary was £30,800 with the highest being £60,000.*

* Known destinations of full-time home postgraduates 2016/17. Salaries are calculated based on the median of those in full-time paid employment within the UK.

Career prospects and employability

University of Nottingham is consistently named as one of the most targeted universities by Britain’s leading graduate employers – ranked in the top 10 in The Graduate Market 2013-2019, High Fliers Research.

The pace of change in the financial services sector has never been faster than it is today. Rapid technological advances combined with ever heightening customer expectations require firms and their employees to generate ideas, calculate business cases and build prototypes faster than ever before. The MSc Financial and Computational Mathematics at the University of Nottingham provides all the fundamental building blocks for a successful employee working in an analytical role in this challenging environment.

Darren Carlile, Capital One


MSc Financial and Computational Mathematics at the University of Nottingham is an excellent programme run by one of the top mathematics departments in the UK. It blends mathematics, finance and computing in a natural and consistent way and equips its graduates very well with knowledge and skills required for quantitative jobs in the financial sector.

Dr Maria Krivko, Quantitative Analyst at a world-leading financial firm 


Those who take up a postgraduate research opportunity with us will not only receive support in terms of close contact with supervisors and specific training related to your area of research, you will also benefit from dedicated careers advice from our  Careers and Employability Service

Our  Careers and Employability Service offers a range of services including advice sessions, employer events, recruitment fairs and skills workshops – and once you have graduated, you will have access to the service for life.


This online prospectus has been drafted in advance of the academic year to which it applies. Every effort has been made to ensure that the information is accurate at the time of publishing, but changes (for example to course content) are likely to occur given the interval between publishing and commencement of the course. It is therefore very important to check this website for any updates before you apply for the course where there has been an interval between you reading this website and applying.

Explore it - Virtual Nottingham
Get in touch
0115 9514954
Make an enquiry


 Michael Tretyakov
Science videos

Science videos


Student Recruitment Enquiries Centre

The University of Nottingham
King's Meadow Campus
Lenton Lane
Nottingham, NG7 2NR

t: +44 (0) 115 951 5559
f: +44 (0) 115 951 5812
w: Frequently asked questions
Make an enquiry