MSc Quantitative Methods for Risk Management

The MSc Quantitative Methods for Risk Management offers world-class training in mathematical, statistical, and machine learning methods for the modelling and analysis of risk in financial markets and beyond.

This programme has been created in response to industry’s strong demand for experts with a deep understanding of risk and a modern quantitative toolset blending mathematical modelling, statistics, and machine learning.

The core part of the programme will offer in-depth instruction in the theory and application of stochastic processes, fundamental statistical methods for risk management, and modern computational techniques for challenging problems in quantitative finance and insurance.

Beyond the core component, you can make the most of the LSE’s world-class Departments of Statistics, Mathematics, and Finance, as you'll have the opportunity to take cutting-edge courses in statistics, data science, mathematical modelling, and finance.

You'll learn to handle real financial data, and, through case studies, you'll get hands-on training in solving real-world problems in finance and insurance.

The programme will prepare you for a range of expert careers in the financial and insurance industries as well as in applied or theoretical research and in regulatory bodies.

• S Shreve Stochastic Calculus for Finance I: the binomial asset pricing model (Springer, 2004)
• J C Hull Risk Management and Financial Institutions (Wiley, 2012)

You'll take a compulsory two-week pre-sessional course in Financial Mathematics before the start of the programme. You'll then take courses to the value of four full units in total, made up of compulsory and optional courses. The three compulsory courses lay the foundations in advanced probabilistic models and statistics methods and give a broad introduction to theories of risk in insurance and finance. For the optional courses, you can choose from courses in statistics, mathematics and finance. You'll choose options to the value of two and a half units in total.

The Department of Statistics at LSE is one of the oldest and most distinguished in the UK.

The department has an international reputation for the development of statistical methodology and a long history of pioneering contributions to research and teaching. Many of the world’s most famous and innovative statisticians have been associated with LSE.

Our research spans four main areas – data science, probability in finance and insurance, social statistics, and time series and statistical learning. The department has close links with the Data Science Institute at LSE – an interdisciplinary institute that fosters the study of data science, with a particular emphasis on the social, economic and political contexts.

All aspects of our research were ranked world-leading or internationally excellent in the most recent Research Excellence Framework (2021), submitted jointly with the Department of Mathematics.

The department’s research strengths are reflected in our teaching curriculum. We have a comprehensive range of undergraduate and postgraduate degrees and doctoral research opportunities – hosted in a vibrant department with a supportive and friendly community of staff and students.

Our alumni enjoy successful careers in diverse areas, such as banking, accounting, finance, statistics, government and business consulting while others pursue postgraduate study or research. Our alumni frequently return to LSE to share their career experiences with current students.

Learn more about our programmes and research.

Department of Statistics

We welcome applications from all suitably qualified prospective students. At LSE, we want to recruit students with the best academic merit, potential and motivation, irrespective of background.

We carefully consider each application and take into account all the information included on your application form, such as your:

• academic achievement (including predicted and achieved grades)
• statement of academic purpose
• two academic references
• CV.

See further information on supporting documents.

You may need to provide evidence of your English language proficiency. See our English language requirements.

Personal statement requirements

Your personal statement should state why you want to do the programme applied for and why you have chosen LSE. Brief details of your academic background and aspirations are also useful. If your background is outside of mathematics or statistics then you should provide further explanation of how your experience is relevant to the programme applied for; as well as further details of your current studies.

If you're applying to study on a part-time basis, please ensure that you address the following in your statement of academic purpose:

• your motivations to study part-time
• how you'll balance the demands of studying with part-time work (if applicable)
• confirmation that you have the support of your employer (if applicable).

Your personal statement should be concise and should not exceed 500 words.

If you're applying for more than one choice in the Department of Statistics, it's recommended that you submit two separate personal statements. If the two programmes for which you are applying are very similar and you'd prefer to combine the information in one statement then you may do so; however, please ensure that your statement clearly addresses your motivations for applying for each separate programme.

As well as the required documents listed above, you should also provide course descriptions and reading lists for advanced courses in mathematics and statistics in your degree (either held or pending).

Applications for this programme are considered on a rolling basis. This means that applications will close once the programme is full.

There is no fixed deadline. However, if you’d like to be considered for any funding opportunities, you must submit your application (and all supporting documents) by the funding deadline. See the fees and funding section below for more details.

For this programme, the tuition fee is different for home and overseas students depending on their fee status.

For this programme, the tuition fee is different for home and overseas students depending on their fee status.

All taught courses are required to include formative coursework which is unassessed. It's designed to help prepare you for summative assessment which counts towards the course mark and to the degree award. LSE uses a range of formative assessment, such as essays, problem sets, case studies, reports, quizzes, mock exams and many others.

Summative assessment may be conducted during the course or by final examination at the end of the course. Most courses are assessed by a two-hour exam in the Spring Term although please note that for ST429 Statistical Methods for Risk Management and ST435 Advanced Probability Theory, exams take place in Winter Term Week 0. Some courses contain an element of course work, such as project and presentation.

An indication of the formative coursework and summative assessment for each course can be found in the relevant course guide.

The programme offers excellent prospects for employment and further study. You can gain employment in the finance or insurance industries, or go on to do a higher degree. Our alumni have taken up positions in banks, asset management firms, insurance and reinsurance companies, data analytics companies, consulting firms, and world-wide research institutions.

Further information on graduate destinations for this programme

From CV workshops through to careers fairs, LSE offers lots of information and support to help you make that all-important step from education into work.

Many of the UK’s top employers give careers presentations at the School during the year and there are numerous workshops covering topics such as job hunting, managing interviews, writing a cover letter and using LinkedIn.

In addition, each year, practitioners from the main finance/insurance firms are invited to give talks and interact with students. In some cases, this can lead to internship opportunities.

See LSE Careers for further details.