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.
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 consistently high rankings in the last two Research Assessment Exercises.
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.
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.
Your industrial placement
Placements normally commence shortly after completion the taught part of the MSc (June) or after completion of the short dissertation (August) and vary in length from three months to 50 weeks, extending the MSc programme to between 15 and 24 months. The start date and duration depend on the employer. Students on a longer placement module (i.e. 6, 9 or 12 months) can transfer to a shorter module if the placement arrangement changes unavoidably once the student has embarked on it, but if a student cannot complete the minimum of three months placement he/she will be required to transfer to the MSc programme without a placement. Placements may be undertaken in the UK or overseas.
The University does not guarantee every student will find a placement. Those who do not secure a placement will be transferred to the MSc programme without a placement.
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.
|Compulsory modules currently include||Credits|
MA881 - Probability and Classical Inference
This course introduces (and revises for some students) the essentials of probability and classical (frequentist) statistical inference, which provide the backbone for later modules.
Syllabus: Probability: axioms, marginal, joint and conditional distributions, Bayes theorem, important distributions, generating functions and various modes of convergence. Classical Inference: Sampling
distributions. Point estimation: consistency, Cramer-Rao inequality, efficiency, sufficiency, minimum variance unbiased estimators. Likelihood. Methods of estimation. Hypothesis tests: maximum likelihood-ratio test, Wald and score tests, profile and test-based confidence intervals.View full module details
MA882 - Advanced Regression Modelling
Linear model. Least squares. General linear model; simple and multiple regression, polynomial regression. Model selection, residuals, outliers, diagnostics. Analysis of variance. Generalised linear model.
Discrete data analysis. Review of Binomial, Poisson, negative binomial and multinomial distributions. Properties, estimation, hypothesis tests.
Contingency tables. Tests for independence. Measures of association. Logistic models.
Multidimensional tables. Log–linear models; fitting and model selection.View full module details
MA883 - Bayesian Statistics
Bayes Theorem for density functions; Conjugate models; Predictive distribution; Bayes estimates; Sampling density functions; Gibbs and Metropolis-Hastings samplers; Winbugs/OpenBUGS; Bayesian hierarchical models; Bayesian model choice; Objective priors; Exchangeability; Choice of priors; Applications of hierarchical models.View full module details
MA886 - Financial Econometrics
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 the residuals of a time series after estimation. Forecasting: Holt-Winters, Box-Jenkins, Prediction bounds. 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.View full module details
MA942 - Data Science with R
Introduction: Machine learning and data visualisation with R.
Classification and prediction: Generalised linear model (GLM), linear discrimination analysis (LDA), k-nearest neighbors (KNN). R-based worked examples.
Resampling methods: Cross-validation (CV) and bootstrap. R-based worked examples.
Regression tree-based methods: Classification and regression trees (CART), bagging, random forests and boosting. R-based worked examples.
Support vector machines (SVM): Support vector classifier, regression SVM. R-based worked examples.
Machine Learning in Action:
(a) Biomedical and health data analysis;
(b) Bond default data analysis;
(c) Insurance data analysis;
(d) Financial data analysis;
(e) Other big data analysis.View full module details
MA975 - Short Dissertation (Statistics)
The module 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. There is no specific syllabus for this module.View full module details
MA976 - Industrial Placement Report and Presentation
Students spend a period 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 earlier during their degree programme. Employer evaluation, personal and professional reviews and on-line blogs are assessed.View full module details
MA991 - Industrial Placement Experience
Students spend a period of time 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 MSc 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 completing the taught component of their studies. The University does not guarantee that every student will find a placement.
Students who do not obtain a placement will be required to transfer to the appropriate programme without an Industrial Placement.View full module details
|Optional modules may include||Credits|
MA6529 - Statistical Learning
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.View full module details
MA835 - Financial Economics and Asset and Liability Modelling
The aim of this module is to provide a grounding in the principles of modelling as applied to actuarial work – focusing particularly on stochastic asset liability models. These skills are also required to communicate with other financial professionals and to critically evaluate modern financial theories.
Indicative topics covered by the module include theories of financial market behaviour, measures of investment risk, stochastic investment return models, asset valuations, and liability valuations.
The additional 4 contact hours for level 7 students will be devoted to applications of the principles of financial economics and asset and liability modelling to complex financial instruments.
This module will cover a number of syllabus items set out in Subject CM2 – Actuarial Mathematics published by the Institute and Faculty of Actuaries.View full module details
MA836 - Stochastic Processes
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.
In addition, level 7 students will study more complex queuing systems and continuous-time branching processes.View full module details
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.View full module details
Teaching and Assessment
Assessment is through coursework involving: complex theoretical questions, analysis of real-world data using appropriate computing packages with a range of levels of guidance appropriate to postgraduate level and over a range of areas of application; Students will be expected to orally present initial work on their projects and during the Practical Statistics module; Dissertation.
Additionally, for the programme with industrial placement, the experience of applying skills in a working environment will be assessed through the placement modules.
Continuation to industrial placement
The placement consists of two modules: Industrial Placement Experience and Industrial Placement Report. Four versions of the Experience module exist to cover placements of different lengths. The Experience module is assessed as pass/fail only and the Report module is graded on a categorical scale.
- 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
- 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.
- 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.
- 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.
- 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.
- Practical experience of the application in a working environment of knowledge and skills gained through academic study.
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 and 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, professional qualifications and experience will also be taken into account.
Please see our International Student website for entry requirements by country and other relevant information for your country. Please note that international fee-paying students cannot undertake a part-time programme due to visa restrictions.
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 2019/20 annual tuition fees for this programme are:
|Statistics with Finance with an Industrial Placement - 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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