Programmes

Statistical Methods in Risk Management

  • Summer schools
  • Department of Statistics
  • Application code SS-ME317
  • Starting 2020
  • Short course: Open
  • Location: Houghton Street, London

UPDATE: Due to the global COVID-19 pandemic we will no longer be offering this course in summer 2020. Please check our latest news on this situation here.  

You can still register your interest in this course for 2021 using the ‘Sign up’ button to the right.  

A self-contained introduction to statistical methods in risk management. This course combines theory and implementation, and emphasises hands-on experience working with real financial data.

The implementation of sound quantitative risk models is a vital task for all financial institutions, and this trend has accelerated in recent years after the last financial crisis. This course provides a self-contained introduction to both theoretical and practical implementation of various techniques in risk management. We draw on diverse quantitative disciplines, from probability to statistics, from actuarial science to quantitative finance. Main topics include: risk factor models, risk measures and their statistical estimation, multivariate factor models, dimensional reduction techniques, copulas, measure of dependence on extreme events. We work with real financial data and aim to provide hands-on experience on practical applications.

This course is designed for third year undergraduates, postgraduates, and professionals who are interested in analytical techniques in risk management.


Session: Three
Dates: 3 August – 21 August 2020
Lecturers: Dr Beatrice Acciaio and Dr Gelly Mitrodima


 

Programme details

Key facts

Level: 300 level. Read more information on levels in our FAQs

Fees:  Please see Fees and payments

Lectures: 36 hours 

Classes: 18 hours

Assessment*:  an individual project and a two-hour written examination.

Typical credit**: 3-4 credits (US) 7.5 ECTS points (EU)


*Assessment is optional

**You will need to check with your home institution

For more information on exams and credit, read Teaching and assessment

Prerequisites

At least one semester of calculus, and at least one semester of probability and statistics. Some understanding in financial markets.

Programme structure

This course is a self-contained introduction to probabilistic and statistical methods in risk management. It aims to provide hands-on experience implementing these methods.

The course consists of 36 hours of lectures supported by 18 hours of computer-based practical classes, which will allow course participants to implement the lecture material in R.

This course starts with risk factor models and loss distributions, illustrated via various examples in stock, derivative, and bond portfolios. Notion of coherent risk measures are introduced. Value at risk and its statistical estimation are presented. Multivariate factor models are introduced and analysed: covariance and correlation estimates, multivariate normal distributions and their testing, dimensional reduction techniques. The theory of copulas is introduced: meta distributions, tail dependence, fitting copulas to data, measure of dependence on extreme events. The extreme value theory is briefly introduced at the end.

All theoretical materials will be implemented on R using real financial and insurance data.

Course outcomes

After completing this course students will:

  • Gain knowledge of important analytical techniques to measure risk in financial market.
  • Develop skills to implement these techniques on real financial data.
  • Gain experience transferring these techniques to other applications.

Teaching

Department of Statistics at LSE has a distinguished history. Its roots can be traced back to the appointment of Sir Arthur Lyon Bowley, an alumnus of the University of Cambridge, at LSE in 1895. He was appointed Chair in Statistics in 1919, probably the first appointment of its kind in Britain. The Department of Statistics was submitted jointly to REF 2014 with LSE's Department of Mathematics: 84% of the research outputs of the two departments were classed as either world-leading or internationally excellent in terms of originality, significance and rigour.

The department has an international reputation for development of statistical methodology that has grown from its long history of active contributions to research and teaching in statistics for the social sciences.

On this three week intensive programme, you will engage with and learn from full-time lecturers from the LSE’s statistics faculty.

Reading materials

Lecture slides and necessary materials will be provided.

Main text:
A.McNeil, R.Frey, P.Embrechts, Quantitative Risk Management: Concepts, Techniques, Tools; Princeton Series in Finance

*A more detailed reading list will be supplied prior to the start of the programme
**Course content, faculty and dates may be subject to change without prior notice

Software used
R, no previous experience is expected.

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How to Apply

Related Programmes

Statistical Methods for Multivariate Data in Social Science Research

Code(s) SS-ME303

Introduction to Calculus

Code(s) SS-ME100

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