Statistics with Applications to Finance

The first two semesters of your course will consist of taught modules and in the third semester you will devote your time to a major dissertation. Within each semester you will take three compulsory modules. However, you can tailor the course to meet your individual needs through selection of a further module in each semester from a variety of options.

In the first semester you’ll be introduced to statistical computing with an emphasis on sampling-based methods including Markov Chain Monte Carlo. You’ll also explore financial investments such as stocks and shares and develop the necessary probabilistic tools to evaluate risks within the market.

In semester two you’ll look at risk assessment in detail, gaining comprehensive knowledge 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. You’ll also learn about pricing financial assets and forecasting future values within a time series.

Your third semester will be taken up by a dissertation in statistics. This will consist of 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.

Course structure

These are typical modules/components studied and may change from time to time. Read more in our Terms and conditions.

Modules Year 1

Compulsory modules

• Discrete Time Finance 15 credits
• Continuous Time Finance 15 credits
• Risk Management 15 credits
• Time Series and Spectral Analysis 15 credits
• Statistical Computing 15 credits
• Dissertation in Statistics 60 credits

Optional modules

• Linear Regression and Robustness 15 credits
• Statistical Theory 15 credits
• Multivariate Analysis 10 credits
• Bayesian Statistics 10 credits
• Generalised Linear Models 10 credits
• Introduction to Statistics and DNA 10 credits
• Models in Actuarial Science 15 credits
• Computations in Finance 15 credits
• Linear Regression, Robustness and Smoothing 20 credits
• Multivariate and Cluster Analysis 15 credits
• Bayesian Statistics and Causality 15 credits
• Generalised Linear and Additive Models 15 credits
• Independent Learning and Skills Project 15 credits
• Statistics and DNA 15 credits

For more information on typical modules, read Statistics with Applications to Finance MSc in the course catalogue

Learning and teaching

This programme is taught jointly between the School of Mathematics and the Leeds University Business School. You will study a mixture of modules taught by specialists in each school though a combination of lectures and small group workshops.

Assessment

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 a short oral presentation.