杏吧论坛

Ms Xiaolin Zhu

This course is compulsory on the MSc in Quantitative Methods for Risk Management. This course is available on the Global MSc in Management, Global MSc in Management (CEMS MIM), Global MSc in Management (MBA Exchange), MSc in Data Science, MSc in Financial Mathematics, MSc in Statistics (Financial Statistics) and MSc in Statistics (Financial Statistics) (Research). This course is available as an outside option to students on other programmes where regulations permit.

This course has a limited number of places (it is controlled access) and demand is typically very high. Priority is given to students on the MSc in Quantitative Methods for Risk Management programme, students from outside this programme may not get a place.

Students must have completed Probability, Distribution Theory and Inference (ST202) and Stochastic Processes (ST302), or equivalent. 

Previous programming experience would be helpful and students who have no previous experience in R must complete an online pre-sessional R course from the Digital Skills Lab before the start of the course (https://moodle.lse.ac.uk/course/view.php?id=7745).

This course covers fundamental definitions of loss functions involving risk factors and risk factor changes. These concepts will be illustrated with examples of different value functions. For the quantitative analysis of the losses of a portfolio we introduce risk measures: General overview from variance to expected shortfall. We concentrate in highly important risk measures: Value at Risk (VaR) and Expected Shortfall (ES).

Considering a portfolio we analyse the distribution and dependence between different risks. We cover multivariate models and Copula models: Sklar's Theorem, Fundamental copulas, Clayton copulas, Archimedean copulas, Dependence measures. As part of dimension reduction we also study Principal component analysis. Finally, we also look at the tail of the distributions and study extreme value theory.

This course will be delivered through a combination of classes, lectures and Q&A sessions totalling a minimum of 30 hours during Autumn Term. This course includes a reading week in Week 6 of Autumn Term.

A set of exercises which are similar to problems appearing in the exam will be assigned. A set of coding exercises which are similar to examples in computer lab sessions will be assigned.

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

Exam (75%, duration: 2 hours) in the January exam period.
Project (25%) in the AT.