MSc in Statistics with Applications to Finance

A taught masters course in statistics combining in-depth training in mainstream advanced statistical modelling with a specialisation in financial mathematics.

Commencing: September 2011

How to Apply:You can apply online by visiting http://www.leeds.ac.uk/students/apply/htm, where you can also download a form for completion by hand.

Want to know more? Continue reading this page, or contact


Ms Iwona Malinowska
Taught Postgraduate Administrator
School of Mathematics
Telephone: + 44(0) 113 343 5111
email: maths-msc@maths.leeds.ac.uk

Contact a lecturer:

Dr Stuart Barber
email: stuart@maths.leeds.ac.uk
Telephone: +44 (0) 113 343 5146


Course overview

The MSc in Statistics with Applications to Finance at the University of Leeds is a focussed degree programme enabling students from a wide range of backgrounds to both broaden and deepen their understanding of statistics and financial applications.

The programme provides training in a core of statistical techniques (and transferable skills) suitable for either careers in statistical finance or for further academic research.

The course is a full-time MSc lasting 12 months, starting in September. The Diploma is just over 8 months duration. The Course consists of two semesters of taught modules, with the third semester (MSc only) devoted to a major dissertation. Within each semester there are three compulsory modules. However, students tailor the course to meet their individual needs through selection of a further module in each semester from a variety of options.

Why study for an MSc?

There is a shortage of well-qualified statisticians in the UK and other countries. Numeracy, in general, is an attribute keenly sought after by employers.

The emergence of data mining and analysis means that demand for statisticians is growing across a wide range of professions, in particular in actuarial companies, betting and gaming industries, and other financial organisations. The course is designed specifically to meet this demand.

Many statistical careers require people educated to masters degree level. This course is designed to build on existing mathematical skills and deepen knowledge of statistics in order for you to access a variety of professions or pursue further research as a PhD student.


Why study Statistics at the University of Leeds?

Simply because you will be taught by leading figures in their field of specialisation! As well as the expertise within the Statistics Department, the MSc also draw on the experience of the centre in Financial Mathematics, a joint venture between the School of Mathematics and Leeds University Business School headed by Professor Klaus Schenk-Hoppé

Our School’s reputation is recognised worldwide through research collaborations with other universities, with government bodies, sectors of industry, hospitals, and through a variety of interdisciplinary projects with other departments within the University.

Our Department is home to LASR - Leeds Annual Statistics Research workshop - an event originated (and still led) by Professor Kanti Mardia over 25 years ago. It has grown from a small group of specialists sharing common interests in development of their research into an international platform attracting experts at the cutting-edge of statistical research.

Other areas of research include:

  • Modern Data Analysis including robustness (a key concept in modern statistical modelling); pattern recognition and machine learning; statistical computation and wavelets.
  • Statistical Bioinformatics including phylogeny; sequential analysis; structure analysis and gene expression analysis.
  • Probability and Stochastic Processes comprising parameter estimation for Markov processes; stochastic financial models; stochastic differential equations; propagation and extinction in catalytic media; intermittency in random media; limit theorems for sums and extreme values and probabilistic methods in number theory and combinatorics.

Course details

Semester One

Compulsory modules:
  • Statistical Computing
    An introduction to methods of statistical computing. Essential for the applied statistician, with an emphasis on sampling-based methods, such as Markov chain Monte Carlo.

  • Stochastic Financial Modelling
    Financial investments such as stocks and shares are risky: their value can go down as well as up. To compensate for the risk in a fair market, a discount is needed. This module will develop the necessary probabilistic tools to enable investors to value such assets.

  • Discrete Time Finance
    This module develops a general methodology for the pricing of financial assets in risky financial markets based on discrete time models.

Optional Modules

  • Statistics and DNA
    Modern biological experiments produce large data sets involving information related to DNA. This module gives the basic biological background before looking at a range of data types and methods to analyse them. Among others, we will look at topics in evolution, genetics, and forensic science.

  • Robust Regression and Smoothing
    A fundamental statistical tool is the simple linear regression model, which predicts the value of a normally-distributed response variable from a predictor variable. This module explores many ways to extend this simple model to cope with non-linear relationships and data corrupted by non-normal errors

Semester Two

Compulsory modules:

  • Time Series and Spectral Analysis
    In time series, measurements are made at a succession of times, and it is the dependence between measurements taken at different times which is important. We concentrate on techniques for model identification, parameter estimation and forecasting future values of the time series.

  • Continuous Time Finance
    Continuous time models play a central role in pricing of financial assets under more challenging circumstances than can be handled with discrete time models.

  • Risk Management
    This module gives comprehensive coverage of mathematical and practical approaches to financial risk management. Avoiding the disastrous consequences of badly managed risk requires detailed mathematical knowledge of how to quantify financial risk and stress-test a hedge.

Optional modules:

  • Independent Learning Skills
    An introduction to research methods including literature search, writing styles, mathematical typesetting and programming skills.

  • Generalised Linear Models and Survival Analysis
    The usual linear regression model deals well with normally distributed data, but what about data where the response is a categorical variable? We see how to cope with binomial and Poisson distributions as part of a wider regression framework, the generalised linear model, and also how to adapt to the reliability or survival data common in medical and actuarial settings.

  • Statistical Theory
    We often use statistical tests and estimators without fully exploring the theoretical basis for their use. Here, we look more deeply into the mathematics behind statistical inference and compare the two main approaches to statistics: Frequentist and Bayesian inference.

Semester Three

  • Project in Statistics

    A three-month research project undertaken in the Summer on a topic (chosen in conjunction with project supervisors) culminating in a dissertation on that project.

The taught course is primarily assessed by end-of-semester examinations with a small component of continuous assessment. The Semester Three project is assessed by a written dissertation and short oral presentation.


Other Information

Opportunities for postgraduate research in the Department of Statistics

Department of Statistics web pages


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