Actuarial Science

Financial Mathematics with a Year in Industry - BSc (Hons)

UCAS code NG31

2019

Our modern world is heavily reliant on financial markets. Financial institutions depend on skilled individuals to manage their portfolios, applying mathematical modelling, statistical analysis and the problem-solving know-how of mathematics graduates.

2019

Overview

Our Year in Industry programme enables you to gain paid industry experience in addition to being taught by our internationally-renowned mathematicians, statisticians and actuaries to ensure you are fully prepared for your future career.

You will be encouraged to fulfil your potential whilst studying in our friendly and dynamic school based in the multi-award-winning Sibson Building.

Our degree programme

To help bridge the gap between school and university, you’ll attend small group tutorials in Stage 1, where you can practice the new mathematics you’ll be learning, ask questions and work with other students to find solutions. You’ll study a mixture of pure and applied mathematics, statistics and economics, providing you with a solid foundation for your later studies.

In Stage 2, you study some core modules which build upon the material learnt at Stage 1. You also start to tailor your degree to your interests through our range of optional modules.

Throughout Stages 1 and 2, you attend specialist programme of workshops and events designed to ensure you have the best possible opportunity of securing a placement. Our in-house Placements Team will support you throughout the process. If you successfully secure a placement, you will spend a year working between Stages 2 and 3.  

In Stage 3 you return from your placement and continue to explore the areas you enjoy through a mixture of core and optional modules. 

Student view

Macaulee shares his experiences studying BSc Financial Mathematics at Kent. 


Foundation year

If your grades do not qualify you for direct entry to this programme, you may be able to take a four-year degree with a foundation year. For more details see Mathematics including a Foundation Year

Study resources

You have access to a range of professional mathematical and statistical software such as:

  • Maple
  • MATLAB
  • Minitab.

Our staff use these packages in their teaching and research.

Extra activities

The School of Mathematics and Actuarial Science Student Society is run by students. It aims to improve the student experience for its members, socially and academically. In previous years the Society has organised:

  • talks and workshops
  • extra revision sessions
  • socials and networking events.
  • seminars and workshops employability events.

The School of Mathematics, Statistics and Actuarial Science also puts on regular events that you are welcome to attend. In the past, these have included:

  • seminars and workshops
  • employability events.

Independent rankings

Mathematics at Kent scored 91.5 out of 100 in The Complete University Guide 2019.

In the National Student Survey 2018, over 87% of final-year Mathematics and Statistics students who completed the survey, were satisfied with the overall quality of their course.

Of Mathematics and Statistics students who graduated from Kent in 2017 and completed a national survey, over 95% were in work or further study within six months (DLHE).

Teaching Excellence Framework

Based on the evidence available, the TEF Panel judged that the University of Kent delivers consistently outstanding teaching, learning and outcomes for its students. It is of the highest quality found in the UK.

Please see the University of Kent's Statement of Findings for more information.

TEF Gold logo

Course structure

The course structure below gives a flavour of the modules that will be available to you and provides details of the content of this programme. This listing is based on the current curriculum and may change year to year in response to new curriculum developments and innovation. Most programmes will require you to study a combination of compulsory and optional modules.

Stage 1

Modules may include Credits

Introduction to R and investigating data sets. Basic use of R (Input and manipulation of data). Graphical representations of data. Numerical summaries of data.

Sampling and sampling distributions. ?² distribution. t-distribution. F-distribution. Definition of sampling distribution. Standard error. Sampling distribution of sample mean (for arbitrary distributions) and sample variance (for normal distribution) .

Point estimation. Principles. Unbiased estimators. Bias, Likelihood estimation for samples of discrete r.v.s

Interval estimation. Concept. One-sided/two-sided confidence intervals. Examples for population mean, population variance (with normal data) and proportion.

Hypothesis testing. Concept. Type I and II errors, size, p-values and power function. One-sample test, two sample test and paired sample test. Examples for population mean and population variance for normal data. Testing hypotheses for a proportion with large n. Link between hypothesis test and confidence interval. Goodness-of-fit testing.

Association between variables. Product moment and rank correlation coefficients. Two-way contingency tables. ?² test of independence.

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15

Time value of money: Basic concepts, Compounding to determine future values, Inflation,

Financial valuation and cash flow analysis: Discounting, Interest rates and time requirements, Future and Present value. Project Evaluation

Characteristics of different financial securities: Debt capital, bonds and stocks, valuation of bonds and stocks

Terminology in finance: Securities markets, primary market, secondary securities markets, the role of the various financial markets.

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15

This module serves as an introduction to algebraic methods and linear algebra methods. These are central in modern mathematics, having found applications in many other sciences and also in our everyday life.

Topics covered will include:

Basic set theory: introduction to sets, operations on sets (union, intersection, Cartesian product, complement), basic counting (inclusion-exclusion for 2 sets).

Functions and Relations: injective, surjective, bijective functions. Permutations, sign of a permutation. The Pigeonhole Principle. Cardinality of sets. Binomial coefficients, Binomial Theorem. Equivalence relations and partitions.

Systems of linear equations and Gaussian elimination: operations on systems of equations, echelon form, rank, consistency, homogeneous and non-homogeneous systems.

Matrices: operations, invertible matrices, trace, transpose.

Determinants: definition, properties and criterion for a matrix to be invertible.

Vector spaces: linearly independent and spanning sets, bases, dimension, subspaces.

Linear Transformations: Definition. Matrix of a Linear Transformation. Change of Basis.

Diagonalisation: Eigenvalues and Eigenvectors, invariant spaces, sufficient conditions.

Bilinear forms: inner products, norms, Cauchy-Schwarz inequality.

Orthonormal systems: the Gram-Schmidt process.

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15

This module introduces widely-used mathematical methods for functions of a single variable. The emphasis is on the practical use of these methods; key theorems are stated but not proved at this stage. Tutorials and Maple worksheets will be used to support taught material.

Complex numbers: Complex arithmetic, the complex conjugate, the Argand diagram, de Moivre's Theorem, modulus-argument form; elementary functions

Polynomials: Fundamental Theorem of Algebra (statement only), roots, factorization, rational functions, partial fractions

Single variable calculus: Differentiation, including product and chain rules; Fundamental Theorem of Calculus (statement only), elementary integrals, change of variables, integration by parts, differentiation of integrals with variable limits

Scalar ordinary differential equations (ODEs): definition; methods for first-order ODEs; principle of superposition for linear ODEs; particular integrals; second-order linear ODEs with constant coefficients; initial-value problems

Curve sketching: graphs of elementary functions, maxima, minima and points of inflection, asymptotes

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15

This module introduces widely-used mathematical methods for vectors and functions of two or more variables. The emphasis is on the practical use of these methods; key theorems are stated but not proved at this stage. Tutorials and Maple worksheets will be used to support taught material.

Vectors: Cartesian coordinates; vector algebra; scalar, vector and triple products (and geometric interpretation); straight lines and planes expressed as vector equations; parametrized curves; differentiation of vector-valued functions of a scalar variable; tangent vectors; vector fields (with everyday examples)

Partial differentiation: Functions of two variables; partial differentiation (including the chain rule and change of variables); maxima, minima and saddle points; Lagrange multipliers

Integration in two dimensions: Double integrals in Cartesian coordinates; plane polar coordinates; change of variables for double integrals; line integrals; Green's theorem (statement – justification on rectangular domains only)

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15

Supply and Demand: Introduction to microeconomics, applying supply and demand, elasticity

Consumer Theory: Preferences and utility, budget constraints, deriving demand curves, applying consumer theory: labour

Producer Theory: Introduction to producer theory, productivity and costs, competition

Welfare Economics: Principles of welfare economics

Monopoly and Oligopoly: Monopoly, oligopoly

Intermediate Topics: Factor markets, international trade, uncertainty, capital supply and demand

Equity and Efficiency: Equity and efficiency, government redistribution policy

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15

Introduction to Probability. Concepts of events and sample space. Set theoretic description of probability, axioms of probability, interpretations of probability (objective and subjective probability).

Theory for unstructured sample spaces. Addition law for mutually exclusive events. Conditional probability. Independence. Law of total probability. Bayes' theorem. Permutations and combinations. Inclusion-Exclusion formula.

Discrete random variables. Concept of random variable (r.v.) and their distribution. Discrete r.v.: Probability function (p.f.). (Cumulative) distribution function (c.d.f.). Mean and variance of a discrete r.v. Examples: Binomial, Poisson, Geometric.

Continuous random variables. Probability density function; mean and variance; exponential, uniform and normal distributions; normal approximations: standardisation of the normal and use of tables. Transformation of a single r.v.

Joint distributions. Discrete r.v.'s; independent random variables; expectation and its application.

Generating functions. Idea of generating functions. Probability generating functions (pgfs) and moment generating functions (mgfs). Finding moments from pgfs and mgfs. Sums of independent random variables.

Laws of Large Numbers. Weak law of large numbers. Central Limit Theorem.

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15

Topics covered will include:

Real Numbers: Rational and real numbers, absolute value and metric structure on the real numbers, induction, countability and uncountability, infimum and supremum.

Limits of Sequences: Sequences, definition of convergence, epsilon terminology, uniqueness, algebra of limits, comparison principles, standard limits, subsequences and non-existence of limits, convergence to infinity.

Completeness Properties: Cantor's Intersection Theorem, limit points, Bolzano-Weierstrass theorem, Cauchy sequences.

Continuity of Functions: Functions and basic definitions, limits of functions, continuity and epsilon terminology, sequential continuity, Intermediate Value Theorem.

Differentiation: Definition of the derivative, product rule, quotient rule and chain rule, derivatives and local properties, Mean Value Theorem, L'Hospital's Rule.

Taylor Approximation: Taylor's Theorem, remainder term, Taylor series, standard examples, O and o notation, limits using Taylor series.

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15

Stage 2

Modules may include Credits

Constructing suitable models for data is a key part of statistics. For example, we might want to model the yield of a chemical process in terms of the temperature and pressure of the process. Even if the temperature and pressure are fixed, there will be variation in the yield which motivates the use of a statistical model which includes a random component. In this module, we study how suitable models can be constructed, how to fit them to data and how suitable conclusions can be drawn. Both theoretical and practical aspects are covered, including the use of R.

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15

In this module we will study linear partial differential equations, we will explore their properties and discuss the physical interpretation of certain equations and their solutions. We will learn how to solve first order equations using the method of characteristics and second order equations using the method of separation of variables.

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15

Basic macroeconomic concepts: Output and income, Unemployment, Inflation and deflation

Macroeconomic models: Aggregate demand–aggregate supply, IS–LM, Growth models

Macroeconomic policy: Monetary policy, Fiscal policy

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15

This module is a pre-requisite for many of the other statistics modules at Stages 2, 3 and 4, but it can equally well be studied as a module in its own right, extending the ideas of probability and statistics met at Stage 1 and providing practice with the mathematical skills learned in MA348 and MA349. It starts by revising the idea of a probability distribution for one or more random variables and looks at different methods to derive the distribution of a function of random variables. These techniques are then used to prove some of the results underpinning the hypothesis test and confidence interval calculations met at Stage 1, such as for the t-test or the F-test. With these tools to hand, the module moves on to look at how to fit models (probability distributions) to sets of data. A standard technique, the method of maximum likelihood, is used to fit the model to the data to obtain point estimates of the model parameters and to construct hypothesis tests and confidence intervals for these parameters. Outline Syllabus includes: Joint, marginal and conditional distributions of discrete and continuous random variables; Transformations of random variables; Sampling distributions; Point and interval estimation; Properties of estimators; Maximum likelihood; Hypothesis testing; Neyman-Pearson lemma; Maximum likelihood ratio test.

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15

This module is an introduction to the methods, tools and ideas of numerical computation. In mathematics, one often encounters standard problems for which there are no easily obtainable explicit solutions, given by a closed formula. Examples might be the task of determining the value of a particular integral, finding the roots of a certain non-linear equation or approximating the solution of a given differential equation. Different methods are presented for solving such problems on a modern computer, together with their applicability and error analysis. A significant part of the module is devoted to programming these methods and running them in MATLAB.

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15

Many problems in finance can be seen as an optimisation subject to some condition. For example, investors usually hold shares in different companies but the total number of shares that can be held is limited by the available funds. Finding the numbers of shares which maximizes the return on the investment whilst respecting the limit on funds is a problem of optimisation (of the return) subject to a condition (the total funds). In this module you learn a range of techniques to solve optimisations subject to conditions. Both theoretical and practical aspects will be covered. Outline of syllabus: Modelling linear programming applications; Graphical method; Simplex method; dual problems; duality theorem; application of duality; complementarity; sensitivity analysis; dual simplex.

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15

This module introduces the basic ideas to solve certain ordinary differential equations, like first order scalar equations, second order linear equations and systems of linear equations. It mainly considers their qualitative and analytical aspects. Outline syllabus includes: First-order scalar ODEs; Second-order scalar linear ODEs; Existence and Uniqueness of Solutions; Autonomous systems of two linear first-order ODEs.

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15

This module covers aspects of Statistics which are particularly relevant to insurance. Some topics (such as risk theory and credibility theory) have been developed specifically for actuarial use. Other areas (such as Bayesian Statistics) have been developed in other contexts but now find applications in actuarial fields. Stochastic processes of events such as accidents, together with the financial flow of their payouts underpin much of the work. Since the earliest games of chance, the probability of ruin has been a topic of interest. Outline Syllabus includes: Decision Theory; Bayesian Statistics; Loss Distributions; Reinsurance; Credibility Theory; Empirical Bayes Credibility theory; Risk Models; Ruin Theory; Generalised Linear Models; Run-off Triangles.

Marks on this module can count towards exemption from the professional examination CT6 of the Institute and Faculty of Actuaries. Please see http://www.kent.ac.uk/casri/Accreditation/index.html for further details.

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15

This module introduces and explores a range of topics relating to corporate finance which are fundamental to understanding why and how companies raise money to start a business or expand an existing one. The module covers the different ways that the money can be raised, for example from a bank or through a stocks and shares market, and the interest rate or investment return that an investor will expect to receive from a company in order to provide the money required. This is a very practical module to the extent that it will help students develop business awareness in the field of company finance. Reference will often be made to actual happenings in the financial markets in support of the material covered.

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15

Year in industry

The year in industry included in this programme provides you with the opportunity to gain valuable work experience. We can help you to find a placement and support you while you are there.

Modules may include Credits

Students spend a year (minimum 900 hours) doing paid work in an organisation outside the University, usually in an industrial or commercial environment, applying and enhancing the skills and techniques they have developed and studied in the earlier stages of their degree programme.

The work they do is entirely under the direction of their industrial supervisor, but support is provided by the SMSAS Placement Officer or a member of the academic team. This support includes ensuring that the work they are being expected to do is such that they can meet the learning outcomes of the module.

Participation in this module is dependent on students obtaining an appropriate placement, for which support and guidance is provided through the School in the year leading up to the placement. It is also dependent on students progressing from Stage 2 of their studies.

Students who do not obtain a placement will be required to transfer to the appropriate programme without a Year in Industry.

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90

Students spend a year (minimum 900 hours) doing paid work in an organisation outside the University, usually in an industrial or commercial environment, applying and enhancing the skills and techniques they have developed and studied in the earlier stages of their degree programme. Employer evaluation, personal and professional reviews and on-line blogs are assessed under MAST5801 (Industrial Placement Experience) which is a co-requisite of this module. The assessment of this module draws on the experience gained in MAST5801 and is assessed through a Placement Report and Presentation.

The placement work they do is entirely under the direction of their industrial supervisor, but support is provided by the SMSAS Placement Officer or a member of the academic team. This support includes ensuring that the work they are being expected to do is such that they can meet the learning outcomes of this module.

Participation in the placement year, and hence in this module, is dependent on students obtaining an appropriate placement, for which support and guidance is provided through the School in the year leading up to the placement. It is also dependent on students progressing satisfactorily from Stage 2 of their studies.

Students who do not obtain a placement or who fail module MAST5801 (Industrial Placement Experience) will be required to transfer to the appropriate programme without a Year in Industry and any marks obtained on this module will not contribute to their final degree classification.

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30

Stage 3

Modules may include Credits

An investor needs an assortment of tools in their toolkit to weigh up risk and return in alternative investment opportunities. This module introduces various measures of investment risk and optimal investment strategies using modern portfolio theory. Pricing of assets using the classical capital asset pricing model and arbitrage pricing theory are discussed. The theory of Brownian motion is used to analyse the behaviour of the lognormal model of asset prices, which is then compared with the auto-regressive Wilkie model of economic variables and asset prices. Principles of utility theory, behavioural finance and efficient market hypothesis provide the context from an investor's perspective. Outline syllabus includes: Measures of investment risk, Mean-Variance Portfolio Theory, Capital Asset Pricing Model, Arbitrage Pricing Theory, Brownian Motion, Lognormal Model, Wilkie Model, Utility Theory and Stochastic Dominance, Efficient Market Hypothesis and Behavioural Finance.

Marks on this module can count towards exemption from the professional examination CT8 of the Institute and Faculty of Actuaries. Please see http://www.kent.ac.uk/casri/Accreditation/index.html for further details.

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15

Introduction: Principles and examples of stochastic modelling, types of stochastic process, Markov property and Markov processes, short-term and long-run properties. Applications in various research areas.

Random walks: The simple random walk. Walk with two absorbing barriers. First–step decomposition technique. Probabilities of absorption. Duration of walk. Application of results to other simple random walks. General random walks. Applications.

Discrete time Markov chains: n–step transition probabilities. Chapman-Kolmogorov equations. Classification of states. Equilibrium and stationary distribution. Mean recurrence times. Simple estimation of transition probabilities. Time inhomogeneous chains. Elementary renewal theory. Simulations. Applications.

Continuous time Markov chains: Transition probability functions. Generator matrix. Kolmogorov forward and backward equations. Poisson process. Birth and death processes. Time inhomogeneous chains. Renewal processes. Applications.

Queues and branching processes: Properties of queues - arrivals, service time, length of the queue, waiting times, busy periods. The single-server queue and its stationary behaviour. Queues with several servers. Branching processes. Applications.

Marks on this module can count towards exemption from the professional examination CT4 of the Institute and Faculty of Actuaries. Please see http://www.kent.ac.uk/casri/Accreditation/index.html for further details.

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15

Multivariate normal distribution, Inference from multivariate normal samples, principal component analysis, mixture models, factor analysis, clustering methods, discrimination and classification, graphical models, the use of appropriate software.

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15

Futures Markets: Mechanics, Hedging strategies

Interest rates: Type of rates, LIBOR, Repo, Spot, Forward

Determination of Forward and Future Prices: Short and long positions, forward prices

Interest Rate Derivatives and Swaps

Option Markets: Mechanics, Properties, trading strategies

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15

Overview of statistical methods. Stationary time series. Autocovariance and autocorrelation functions. Partial autocorrelation functions. ARMA processes. ARIMA model building, testing and estimation. Criteria for choosing between models. Forecasting. Cointegration. Prediction bounds. Asset return and risk. Term structure of interest rates. Distributional properties of asset returns. Testing for CAPM. Testing random walk hypothesis and predicting asset return. Sharpe ratio and efficient portfolio. Cross-section modelling and GMM. Estimate multifactor models. Financial applications of AR, MA, and ARMA. ARCH and GARCH models. Volatility processes. Simple applications of these techniques using R.

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15

Motivating examples; model fitting through maximum likelihood for specific examples; function optimization methods; profile likelihood; score tests; Wald tests; confidence interval construction; latent variable models; EM algorithm; mixture models; simulation methods; importance sampling; kernel density estimation; Monte Carlo inference; bootstrap; permutation tests; R programs.

In addition, for level 7 students: advanced EM algorithm methods, advanced simulation methods, writing R programs for advanced methods and applications.

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15

The aim of this module is to equip students with the skills needed to communicate mathematics effectively to the world. This module is supported by a series of workshops covering various forms of written and oral communication. Each student will choose a topic in mathematics, statistics or financial mathematics from a published list on which to base their three coursework assessments which include a scientific writing assessment and an oral presentation.

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15

There is no specific mathematical syllabus for this module. Students will study a topic in mathematics or statistics, either individually or within a small group, and produce an individual or group project on the topic as well as individual coursework assignments. Projects will be chosen from published lists of individual and of group projects. The coursework and project-work are supported by a series of workshops covering various forms of written and oral communication and by supervision from an academic member of staff.

The workshops may include critically evaluating the following: a research article in mathematics or statistics; a survey or magazine article aimed at a scientifically-literate but non-specialist audience; a mathematical biography; a poster presentation of a mathematical topic; a curriculum vitae; an oral presentation with slides or board; a video or podcast on a mathematical topic. Guidance will be given on typesetting mathematics using LaTeX.

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30

This is a practical module to develop the skills required by a professional statistician (report writing, consultancy, presentation, wider appreciation of assumptions underlying methods, selection and application of analysis method, researching methods).

Software: R, SPSS and Excel (where appropriate/possible). Report writing in Word. PowerPoint for presentations.

• Presentation of data

• Report writing and presentation skills

• Hypothesis testing: formulating questions, converting to hypotheses, parametric and non-parametric methods and their assumptions, selection of appropriate method, application and reporting. Use of resources to explore and apply additional tests. Parametric and non-parametric tests include, but are not limited to, t-tests, likelihood ratio tests, score tests, Wald test, chi-squared tests, Mann Whitney U-test, Wilcoxon signed rank test, McNemar's test.

• Linear and Generalised Linear Models: simple linear and multiple regression, ANOVA and ANCOVA, understanding the limitations of linear regression, generalised linear models, selecting the appropriate distribution for the data set, understanding the difference between fixed and random effects, fitting models with random effects, model selection.

• Consultancy skills: group work exercise(s)

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15

Combinatorial games, game trees, strategy, classification of positions. Two-player zero-sum games, security levels, pure and mixed strategies, von Neumann's minimax theorem. Solving zero-sum two player games using linear programming. Arbitrary sum games, utility, and matrix games. Nash equilibrium, Nash equilibrium theorem, applications, and cooperation. Multi-player games, coalitions, and the Shapley value.

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15

Most differential equations which arise from physical systems cannot be solved explicitly in closed form, and thus numerical solutions are an invaluable way to obtain information about the underlying physical system. The first half of the module is concerned with ordinary differential equations. Several different numerical methods are introduced and error growth is studied. Both initial value and boundary value problems are investigated. The second half of the module deals with the numerical solution of partial differential equations. The syllabus includes: initial value problems for ordinary differential equations; Taylor methods; Runge-Kutta methods; multistep methods; error bounds and stability; boundary value problems for ordinary differential equations; finite difference schemes; difference schemes for partial differential equations; iterative methods; stability analysis.

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15

This module introduces the main features of basic financial derivative contracts and develops pricing techniques. Principle of no-arbitrage, or absence of risk-free arbitrage opportunities, is applied to determine prices of derivative contracts, within the framework of binomial tree and geometric Brownian motion models. The interplay between pricing and hedging strategies, along with risk management principles, are emphasized to explain the mechanisms behind derivative instruments. Models of interest rate and credit risk are also discussed in this context. Outline syllabus includes: An introduction to derivatives, binomial tree model, Black-Scholes option pricing formula, Greeks and derivative risk management, interest rate models, credit risk models.

Marks on this module can count towards exemption from the professional examination CT8 of the Institute and Faculty of Actuaries. Please see http://www.kent.ac.uk/casri/Accreditation/index.html for further details.

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15

The origins of Bayesian inference lie in Bayes' Theorem for density functions; the likelihood function and the prior distribution combine to provide a posterior distribution which reflects beliefs about an unknown parameter based on the data and prior beliefs. Statistical inference is determined solely by the posterior distribution. So, for example, an estimate of the parameter could be the mean value of the posterior distribution. This module will provide a full description of Bayesian analysis and cover popular models, such as the normal distribution. Initially, the flavour will be one of describing the Bayesian counterparts to well known classical procedures such as hypothesis testing and confidence intervals. Outline Syllabus includes: Bayes Theorem for density functions; Exchangeability; Choice of priors; Conjugate models; Predictive distribution; Bayes estimates; Sampling density functions; Gibbs samplers; OpenBUGS; Bayesian hierarchical models; Applications of hierarchical models; Bayesian model choice.

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15

Discrete mathematics has found new applications in the encoding of information. Online banking requires the encoding of information to protect it from eavesdroppers. Digital television signals are subject to distortion by noise, so information must be encoded in a way that allows for the correction of this noise contamination. Different methods are used to encode information in these scenarios, but they are each based on results in abstract algebra. This module will provide a self-contained introduction to this general area of mathematics.

Syllabus: Modular arithmetic, polynomials and finite fields. Applications to

• orthogonal Latin squares,

• cryptography, including introduction to classical ciphers and public key ciphers such as RSA,

• "coin-tossing over a telephone",

• linear feedback shift registers and m-sequences,

• cyclic codes including Hamming,

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15

Teaching and assessment

Teaching amounts to approximately 16 hours of lectures and classes per week. Modules that involve programming or working with computer software packages usually include practical sessions.

The majority of Stage 1 modules are assessed by end-of-year examinations. Many Stage 2 and 3 modules include coursework which normally counts for 20% of the final assessment. Both Stage 2 and 3 marks count towards your final degree result.

Programme aims

The programme aims to:

  • instil in students the technical appreciation, skills and knowledge required by graduates in financial mathematics
  • develop students’ abilities for rigorous reasoning and precise expression, and formulate and solve problems relevant to financial mathematics
  • encourage an appreciation of recent developments in financial mathematics, and the links between theory and practical application
  • encourage a logical, mathematical approach to solving problems
  • develop an enhanced capacity for independent thought and work
  • ensure students are competent in the use of information technology, and are familiar with computers and the relevant software
  • provide opportunities to study advanced topics in financial mathematics, engage in research, and develop communication and personal skills
  • instil awareness of the application of technical concepts in the workplace (for students undertaking a year in industry).

Learning outcomes

Knowledge and understanding

You gain knowledge and understanding of:

  • core mathematical skills in the principles of calculus, algebra, mathematical methods, discrete mathematics, analysis and linear algebra
  • statistical aspects of probability and inference
  • information technology skills relevant to mathematicians
  • methods and techniques appropriate to financial mathematics
  • logical mathematical argument and deductive reasoning.

Intellectual skills

You develop intellectual skills in the following areas:

  • the ability to demonstrate a reasonable understanding of knowledge in financial mathematics
  • calculation and manipulation of the material within the programme
  • application of concepts and principles in various contexts relevant to financial mathematics
  • a capacity for logical argument
  • problem solving by various methods
  • computer skills
  • the capacity to work with relatively little guidance.

Subject-specific skills

You gain subject-specific skills to:

  • demonstrate knowledge of key mathematical concepts and topics, both explicitly and by applying them to the solution of problems
  • comprehend problems, abstract the essentials of problems and formulate them mathematically and in symbolic form to facilitate their analysis and solution
  • use computational and more general IT facilities as an aid to mathematical processes
  • present mathematical arguments and the conclusions from them with clarity and accuracy.

Transferable skills

You gain transferable skills in the following:

  • problem-solving in relation to qualitative and quantitative information
  • effective communication
  • numeracy and computational abilities
  • information retrieval in relation to primary and secondary information sources, including online computer searches
  • computer skills, such as word-processing and spreadsheet use, internet communication
  • time-management and organisational skills: the ability to plan and implement efficient and effective modes of working
  • continuing professional development.

Careers

The practical experience you gain with this year in industry option gives you a real advantage in the graduate job market. Through your studies, you also acquire many transferable skills including the ability to deal with challenging ideas, think critically, write well and present your ideas clearly, all of which are considered essential by graduate employers.

Recent graduates from the School have gone into careers in medical statistics, the pharmaceutical industry, the aerospace industry, software development, teaching, actuarial work, Civil Service statistics, chartered accountancy, the oil industry and postgraduate research.

Andrew Paul

Maths is a much broader subject than you would think. It’s only when you get to university that you realise how many different topics you can study.

Andrew Paul Mathematics with a Year in Industry BSc

Entry requirements

Home/EU students

The University will consider applications from students offering a wide range of qualifications. Typical requirements are listed below. Students offering alternative qualifications should contact us for further advice. 

It is not possible to offer places to all students who meet this typical offer/minimum requirement.

New GCSE grades

If you’ve taken exams under the new GCSE grading system, please see our conversion table to convert your GCSE grades.

Qualification Typical offer/minimum requirement
A level

ABB including Mathematics at grade A. Use of Maths A level is not accepted as a required subject. Only one of General Studies or Critical Thinking can count as a third A level.

If taking both A level Mathematics and A level Further Mathematics:

ABC including Mathematics at grade A and Further Mathematics at grade C. Use of Maths A level is not accepted as a required subject. Only one of General Studies or Critical Thinking can count as a third A level.

Access to HE Diploma

The University will not necessarily make conditional offers to all Access candidates but will continue to assess them on an individual basis. 

If we make you an offer, you will need to obtain/pass the overall Access to Higher Education Diploma and may also be required to obtain a proportion of the total level 3 credits and/or credits in particular subjects at merit grade or above.

BTEC Level 3 Extended Diploma (formerly BTEC National Diploma)

The University will consider applicants holding BTEC National Diploma and Extended National Diploma Qualifications (QCF; NQF; OCR) on a case-by-case basis. Please contact us for further advice on your individual circumstances.

International Baccalaureate

34 points overall or 16 points at HL including Mathematics 6 at HL

International students

The University welcomes applications from international students programmes. Our international recruitment team can guide you on entry requirements. See our International Student website for further information about entry requirements for your country. 

However, please note that international fee-paying students cannot undertake a part-time programme due to visa restrictions.

If you need to increase your level of qualification ready for undergraduate study, we offer a number of International Foundation Programmes.

Meet our staff in your country

For more advice about applying to Kent, you can meet our staff at a range of international events.

English Language Requirements

Please see our English language entry requirements web page.

Please note that if you are required to meet an English language condition, we offer a number of 'pre-sessional' courses in English for Academic Purposes. You attend these courses before starting your degree programme. 

General entry requirements

Please also see our general entry requirements.

Fees

The 2019/20 annual tuition fees for this programme are:

UK/EU Overseas
Full-time £9250 £15700

For students continuing on this programme, fees will increase year on year by no more than RPI + 3% in each academic year of study except where regulated.* 

Your fee status

The University will assess your fee status as part of the application process. If you are uncertain about your fee status you may wish to seek advice from UKCISA before applying.

Fees for Year in Industry

For 2019/20 entrants, the standard year in industry fee for home, EU and international students is £1,385

Fees for Year Abroad

UK, EU and international students on an approved year abroad for the full 2019/20 academic year pay £1,385 for that year. 

Students studying abroad for less than one academic year will pay full fees according to their fee status. 

General additional costs

Find out more about accommodation and living costs, plus general additional costs that you may pay when studying at Kent.

Funding

University funding

Kent offers generous financial support schemes to assist eligible undergraduate students during their studies. See our funding page for more details. 

Government funding

You may be eligible for government finance to help pay for the costs of studying. See the Government's student finance website.

Scholarships

General scholarships

Scholarships are available for excellence in academic performance, sport and music and are awarded on merit. For further information on the range of awards available and to make an application see our scholarships website.

The Kent Scholarship for Academic Excellence

At Kent we recognise, encourage and reward excellence. We have created the Kent Scholarship for Academic Excellence. 

The scholarship will be awarded to any applicant who achieves a minimum of AAA over three A levels, or the equivalent qualifications (including BTEC and IB) as specified on our scholarships pages

The scholarship is also extended to those who achieve AAB at A level (or specified equivalents) where one of the subjects is either mathematics or a modern foreign language. Please review the eligibility criteria.

The Key Information Set (KIS) data is compiled by UNISTATS and draws from a variety of sources which includes the National Student Survey and the Higher Education Statistical Agency. The data for assessment and contact hours is compiled from the most populous modules (to the total of 120 credits for an academic session) for this particular degree programme. 

Depending on module selection, there may be some variation between the KIS data and an individual's experience. For further information on how the KIS data is compiled please see the UNISTATS website.

If you have any queries about a particular programme, please contact information@kent.ac.uk.