The MSc in Statistics with Finance is accredited by the Royal Statistical Society (RSS) and is excellent preparation for careers in any field requiring a strong statistical background.
This programme trains students for careers using statistics in the financial services industry. You study the statistical modelling underpinning much modern financial engineering combined with a deep understanding of core statistical concepts. The programme includes modelling of financial time series, risk and multivariate techniques.
Statistics at Kent provides:
- a programme that gives you the opportunity to develop practical, mathematical and computing skills in statistics, while working on challenging and important problems relevant to a broad range of potential employers
- teaching and supervision by staff who are research-active, with established reputations and who are accessible, supportive and genuinely interested in your work
- advanced and accessible computing and other facilities
- a congenial work atmosphere with pleasant surroundings, where you can socialise and discuss issues with a community of other students.
About the School of Mathematics, Statistics and Actuarial Science (SMSAS)
The School has a strong reputation for world-class research and a well-established system of support and training, with a high level of contact between staff and research students. Postgraduate students develop analytical, communication and research skills. Developing computational skills and applying them to mathematical problems forms a significant part of the postgraduate training in the School. We encourage all postgraduate statistics students to take part in statistics seminars and to help in tutorial classes.
The Statistics Group is forward-thinking, with varied research, and received high rankings in the Research Excellence Framework (REF) for research power and quality.
In the Research Excellence Framework (REF) 2014, research by the School of Mathematics, Statistics and Actuarial Science was ranked 25th in the UK for research power and 100% or our research was judged to be of international quality.
An impressive 92% of our research-active staff submitted to the REF and the School’s environment was judged to be conducive to supporting the development of world-leading research.
You undertake a substantial project in the area of finance or financial econometrics, supervised by an experienced researcher. Some projects are focused on the analysis of particular complex data sets while others are more concerned with generic methodology.
You gain experience of analysing real data problems through practical classes and exercises. The course includes training in the computer language R.
The following modules are indicative of those offered on this programme. This list 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. You may also have the option to take modules from other programmes so that you may customise your programme and explore other subject areas that interest you.
|Modules may include||Credits|
MA835 - Portfolio Theory and Asset Pricing Models
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.Read more
MA836 - Stochastic Processes
A stochastic process is a process developing in time according to probability rules; for example, models for reserves in insurance companies, queue formation, the behaviour of a population of bacteria, and the persistence (or otherwise) of an unusual surname through successive generations. The module will focus on the idea of a stochastic process, and show how this notion can be combined with probability and matrix to build a stochastic model. It will include coverage of a wide variety of stochastic processes and their applications; random walk; Markov chains; processes in continuous-time such as the Poisson process, the birth and death process and Brownian motion; renewal processes; queues; branching processes; epidemic models.
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.Read more
MA837 - Mathematics of Financial Derivatives
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.Read more
MA889 - Analysis of Large Data Sets
This module considers statistical analysis when we observe multiple characteristics on an experimental unit. For example, a sample of students' marks on several exams or the genders, ages and blood pressures of a group of patients. We are particularly interested in understanding the relationships between the characteristics and differences between experimental units. Regression methods can be used if one characteristic can be treated as a response variable and the others as explanatory variables. Variable selection on the explanatory variables can be daunting if the number of characteristics is large and suitable methods will be investigated. Outline Syllabus includes: measure of dependence, principal component analysis, factor analysis, canonical correlation analysis, hypothesis testing, discriminant analysis, clustering, scaling, information criterion methods for variable selection, false discovery rate, penalised maximum likelihood.Read more
MA890 - Practical Statistics and Computing
Nonparametric Methods: This part of the module comprises approximately 10 lectures on nonparametric methods, showing how they are applied in practice for testing goodness of fit to a distribution, including tests of normality, for testing randomness of a sequence, and for comparing two samples. Practical Statistics: There is no fixed syllabus for this component of the course. Students gain experience of practical data analysis through a series of assessments that confront them with unfamiliar data, which may require the use of techniques introduced in any of the other core modules of the Programme. Statistical Computing: At the start of the module, students are introduced to, and gain experience of, the document preparation system LaTeX, which enables the production of high-quality mathematical documents. Then there are sessions in which students learn the statistical package R, using a mixture of lectures and hands-on computing workshops. The initial aim is for students to gain familiarity with importing and manipulating data, producing graphs and tables, and running standard statistical analyses. The later parts of the module focus on the use of R as a programming language, introducing basic programming mechanisms such as loops, conditional statements and functions. This provides students with the means to develop their own code to undertake non-routine types of analysis if these are not already available in R.Read more
MA867 - Project
The module, which is compulsory for students of MSc in Statistics and MSc in Statistics with Finance, enables students to undertake an independent piece of work in a particular area of statistics, or statistical finance/financial econometrics and to write a coherent account of the material. A list of possible topics, together with names of Staff willing to supervise these projects, will be circulated to students in the autumn term. A broad range of projectsis available, encompassing both practical data analysis and more methodological work, although projects that are primarily theoretical will typically have obvious practical applications. Students then choose a topic after consultation and agreement with the relevant member of staff. This is done early in the spring term and some preliminary work is done during the spring term, leading to a short presentation at the end of that term. The main part of the project is then undertaken after the examinations in May.Read more
MA881 - Probability and Classical Inference
This module begins by introducing probability, primarily as a tool that underlies the subsequent material on statistical inference. This includes, for example, various notions of convergence for random variables. Classical statistical inference assumes that data follow a probability model with some unknown parameters, and the main aims are to estimate these parameters and to test hypotheses about them. The focus of the module is to develop general methods of statistical inference that can be applied to a wide range of problems. Outline syllabus includes: probability axioms; marginal, joint and conditional distributions; Bayes theorem; important distributions; convergence of random variables; sampling distributions; likelihood; point estimation; interval estimation; likelihood-ratio, Wald and score tests; estimating equations.Read more
MA882 - Advanced Regression Modelling
This module covers regression techniques used to understand the effect of explanatory variables on a response, which may be continuous, ordinal or categorical. Issues including general inference, goodness-of-fit, variable selection and diagnostics will be discussed and the material presented in a data-centred way. Outline Syllabus includes: Linear Model: Simple and multiple linear regression including inference (estimation, hypothesis testing and confidence intervals) and diagnostics (detection of outliers, multicollinearity and influential observations). The General linear model, polynomial regression and analysis of variance. Discrete data analysis: Review of Binomial, Poisson, negative binomial and multinomial distributions. Properties, estimation, hypothesis tests. Generalized Linear Model: Estimation, hypothesis testing and model comparison of these models. Diagnostics and goodness-of-fit. Contingency tables: Tests for independence, Measures of association, logistic models, multidimensional tables, log linear models, fitting and model selection.Read more
MA883 - Bayesian Statistics
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. Current methods for inference involving posterior distributions typically involve sampling strategies. That is, due to the complicated nature of some posterior distributions, analytic methods fail to provide meaningful summaries. Hence, sampling from the posterior has become popular. A full description of sampling techniques, starting from rejection sampling, will be given. Outline Syllabus includes: Conjugate models (prior and posterior belong to the same family of parametric models). Predictive distributions; Bayes estimates; Sampling density functions; Gibbs and Metropolis-Hastings samplers; Winbugs; Bayesian regression and hierarchical models; Bayesian model choice; Decision theory; Objective priors; Exchangeability.Read more
MA886 - Modelling of Time-Dependent Data and Financial Econometrics
This module aims to introduce basic statistical and econometrical tools for analysing financial time series data. The syllabus includes: Stationary time series: stationarity, autocovariance and autocorrelation functions, partial autocorrelation functions, ARMA processes. ARIMA model Building and Testing: estimation, Box Jenkins, criteria for choosing between models, diagnostic tests for residuals of a time series after estimation. Forecasting: Holt-Winters, Box-Jenkins, prediction bounds. Financial econometrics: distributional properties of asset returns, regression test for CAPM, multifactor models, financial applications of AR, MA, and ARMA, predicting asset returns, ARCH and GARCH models, random walk hypothesis tests, volatility processes.Read more
Teaching and Assessment
Coursework involving: complex theoretical questions, analysis of real-world data using appropriate computing packages over a range of areas of application; analysis appropriate to financial data (in particular modules); written unseen examinations; dissertation.
The programme aims:
- To give students the depth of technical appreciation and skills appropriate to masters' level students in Statistics.
- To equip students with a comprehensive and systematic understanding of theoretical and practical Statistics, and their uses in Finance.
- To develop students’ capacity for rigorous reasoning and precise expression.
- To develop students’ capabilities to formulate and solve problems relevant to Statistics.
- To develop in students appreciation of recent developments in Statistics, and of the links between the theory of Statistics and financial modelling (and other areas of application).
- To develop in students a logical, mathematical approach to solving problems.
- To develop in students an enhanced capacity for independent thought and work.
- To ensure students are competent in the use of information technology, and are familiar with computers, together with the relevant software.
- To provide students with opportunities to study advanced topics in Statistics, engage in research at some level, and develop communication and personal skills.
- To provide successful students with the depth of knowledge of the subject sufficient to enter a career as a professional statistician or appropriate career in quantitative finance.
- To provide a deep understanding of the use of Statistics in Finance and Financial Econometrics
Knowledge and understanding
You will gain knowledge and understanding of:
- Systematic understanding of probability and statistics and the range of principles involved.
- Awareness of links between different statistical concepts and methods.
- Advanced information technology skills relevant to statisticians.
- A comprehensive range of methods and techniques appropriate to statistics at the postgraduate level.
- The role of logical mathematical argument and deductive reasoning.
- Appreciation of particular subject areas to which statistics is applied, particularly finance, and the importance of the role of statistics in those areas.
- Appreciation of the use of Statistics in Finance and the probabilistic concepts involved.
You develop intellectual skills in:
- Ability to demonstrate a comprehensive understanding of the main body of statistical knowledge.
- Ability to demonstrate skill in calculation and manipulation of data.
- Ability to apply a range of statistical concepts and principles in various challenging contexts.
- Ability for logical argument.
- Ability to demonstrate skill in solving complex statistical problems using appropriate and advanced methods.
- Ability in relevant computer skills and usage.
- Ability to work with relatively little guidance.
- Ability to evaluate research work critically.
You gain subject-specific skills in:
- Ability to demonstrate knowledge of advanced statistical concepts and topics, both explicitly and by applying them to the solution of problems.
- Ability to demonstrate knowledge of statistical modelling techniques commonly applied to finance.
- Ability to abstract the essentials of problems to facilitate statistical analysis and interpretation.
- Ability to present statistical analyses and draw conclusions with clarity and accuracy.
You will gain the following transferable skills:
- Problem-solving skills; ability to work independently to solve problems involving qualitative or quantitative information.
- Communication skills, including the capacity to report to others on analyses undertaken.
- Computational skills.
- Information-retrieval skills involving a range of resources.
- Information technology skills including scientific word-processing.
- Time-management and organisational skills, as evidenced by the ability to plan and implement efficient and effective modes of working.
- Skills needed for continuing professional development.
Students often go into careers as professional statisticians in industry, government, research and teaching but our programmes also prepare you for careers in other fields requiring a strong statistical background. You have the opportunity to attend careers talks from professional statisticians working in industry and to attend networking meetings with employers.
Recent graduates have started careers in diverse areas such as the pharmaceutical industry, financial services and sports betting.
The taught programmes in Statistics with Finance provide exemption from the professional examinations of the Royal Statistical Society and qualification for Graduate Statistician status.
Kent’s Computing Service central facility runs Windows. Within the School, postgraduate students can use a range of UNIX servers and workstations. Packages available include R, SAS, MATLAB, SPSS and MINITAB.
Dynamic publishing culture
Staff publish regularly and widely in journals, conference proceedings and books. Among others, they have recently contributed to: Annals of Statistics; Biometrics; Biometrika; Journal of Royal Society, Series B; Statistics and Computing. Details of recently published books can be found within our staff research interests.
Global Skills Award
All students registered for a taught Master's programme are eligible to apply for a place on our Global Skills Award Programme. The programme is designed to broaden your understanding of global issues and current affairs as well as to develop personal skills which will enhance your employability.
A minimum of 2.2, with a substantial amount of mathematics at university level. Prior experience of finance is not required.
All applicants are considered on an individual basis and additional qualifications, and professional qualifications and experience will also be taken into account when considering applications.
Please see our International Student website for entry requirements by country and other relevant information for your country.
English language entry requirements
The University requires all non-native speakers of English to reach a minimum standard of proficiency in written and spoken English before beginning a postgraduate degree. Certain subjects require a higher level.
For detailed information see our English language requirements web pages.
Need help with English?
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 through Kent International Pathways.
Biometry and ecological statistics
Specific interests are in biometry, cluster analysis, stochastic population processes, analysis of discrete data, analysis of quantal assay data, overdispersion, and we enjoy good links within the University, including the School of Biosciences and the Durrell Institute of Conservation and Ecology. A recent major joint research project involves modelling the behaviour of yeast prions and builds upon previous work in this area. We also work in collaboration with many external institutions.
Current work includes non-parametric Bayes, inference robustness, modelling with non-normal distributions, model uncertainty, variable selection and functional data analysis.
Bioinformatics, statistical genetics and medical statistics
Research covers bioinformatics (eg DNA microarray data), involving collaboration with the School of Biosciences. Other interests include population genetics, clinical trials and survival analysis.
Research focuses on empirical likelihood, high-dimensional data analysis, nonlinear dynamic analysis, semi-parametric modelling, survival analysis, risk insurance, functional data analysis, spatial data analysis, longitudinal data analysis, feature selection and wavelets.
Staff research interests
Full details of staff research interests can be found on the School's website.
Dr Diana Cole: Senior Lecturer in Statistics
Branching processes in biology; cell division models; ecological statistics; generalised linear mixed models; identifiability.; parameter redundancy.View Profile
Dr Fabrizio Leisen: Senior Lecturer in Statistics
Bayesian nonparametrics; MCMC, Urn models; Markov and Levy processes; Move-to-Front and Move-to-Root allocation rules.View Profile
Dr Alfred Kume: Senior Lecturer in Statistics
Shape analysis; directional statistics; image analysis.View Profile
Dr Alexa Laurence: Lecturer in Statistics
Medical statistics and applied statistics.View Profile
Dr Owen Lyne: Lecturer in Statistics
Stochastic epidemic models; applied probability; simulation; statistical inference; goodness of fit; branching processes; martingales; medical education.View Profile
Dr Rachel McCrea: Research Associate
Integrated population modelling of dependent data structures.View Profile
Professor Byron Morgan: Professor of Applied Statistics
Biometry; cluster analysis; stochastic population processes; psychological applications of statistics; multivariate analysis; simulation; analysis of quantal assay data; medical statistics; ecological statistics; overdispersion; estimation using transforms.View Profile
Professor Martin S Ridout: Professor of Applied Statistics
Analysis of discrete data in biology; generalised linear models; overdispersion; stochastic models; transform methods.View Profile
Dr Xue Wang: Lecturer in Statistics
Bayesian nonparametric methods; copula function with its applications in finance; wavelet estimation methods.View Profile
Professor Jian Zhang: Professor of Statistics
Semi and non-parametric statistical modelling; statistical genetics with medical applications; Bayesian modelling; mixture models; neuroimaging.View Profile
The 2018/19 annual tuition fees for this programme are:
|Statistics with Finance - MSc at Canterbury:|
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.* If you are uncertain about your fee status please contact email@example.com
General additional costs
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