ST429      Half Unit
Statistical Methods for Risk Management

This information is for the 2017/18 session.

Teacher responsible

Dr Hao Xing COL 7.12


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 and MSc in Statistics (Financial Statistics). This course is available as an outside option to students on other programmes where regulations permit.


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

ST202, ST302, or equivalent

Course content

A self-contained introduction to probabilistic and statistical methods in risk management. This course starts with risk factors models and loss distributions, which are illustrated via examples in stocks, derivatives, and bonds portfolios. Axioms of coherent risk measures are introduced. Value at risk and other risk measures are introduced and their relation with coherent risk measures is discussed. Multivariate factor models are introduced and analysed: covariance and correlation estimations, multivariate normal distributions and their testing, normal mixture distributions and their fitting to data. The theory of copulas is introduced: meta distributions, tail dependence, fitting copulas to data. Some limitations of copulas are also discussed. The extreme value theory is introduced: generalized extreme value distribution, threshold exceedances and generalized Pareto distribution, modelling and measures of tail risk. Applications to insurance with large loss are also discussed. Students will be exposed to financial data via sets of computer-based classes and exercises.


20 hours of lectures and 10 hours of seminars in the MT.

A exercise/problem-solving session will take place in Week 6.

Formative coursework

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.

Indicative reading

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


Exam (75%, duration: 2 hours) in the LT week 0.
Project (25%, 2000 words).

Student performance results

(2013/14 - 2015/16 combined)

Classification % of students
Distinction 53.8
Merit 23.1
Pass 19.2
Fail 3.8

Key facts

Department: Statistics

Total students 2016/17: 33

Average class size 2016/17: 33

Controlled access 2016/17: No

Lecture capture used 2016/17: Yes (LT)

Value: Half Unit

Guidelines for interpreting course guide information

Personal development skills

  • Team working
  • Application of information skills
  • Application of numeracy skills
  • Commercial awareness