ST436      Half Unit
Financial Statistics

This information is for the 2017/18 session.

Teacher responsible

Prof Piotr Fryzlewicz COL 6.01


This course is compulsory on the MSc in Statistics (Financial Statistics) and MSc in Statistics (Financial Statistics) (Research). This course is available on the MSc in Data Science and MSc in Quantitative Methods for Risk Management. This course is available with permission as an outside option to students on other programmes where regulations permit.


Students must have completed Statistical Inference: Principles, Methods and Computation (ST425).

Course content

The course covers key statistical methods and data analytic techniques most relevant to finance. Hands-on experience in analysing financial data in the “R” environment is an essential part of the course. The course includes a selection of the following topics: obtaining financial data, low- and high-frequency financial time series, ARCH-type models for low-frequency volatilities and their simple alternatives, predicting equity indices (case study), Markowitz portfolio theory and the Capital Asset Pricing Model, machine learning in financial forecasting, Value at Risk, simple trading strategies. The course ends with an extended case study involving making predictions of market movements in a virtual trading environment.


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

Week 11 will be spent working on the extended case study.

Formative coursework

Weekly marked problem sheets, with solutions discussed in class. Two marked case studies.

Indicative reading

Lai, T.L. And Xing H. (2008) Statistical Models and Methods for Financial Markets. Springer. Tsay, R. S. (2005) Analysis of Financial Time Series. Wiley. Ruppert, D. (2004) Statistics and Finance – an introduction. Springer. Fan, Yao (2003) Nonlinear Time Series. Hastie, Tibshirani, Friedman (2009) The Elements of Statistical Learning. Haerdle, Simar (2007) Applied Multivariate Statistical Analysis.


Exam (100%, duration: 2 hours) in the main exam period.

Student performance results

(2013/14 - 2015/16 combined)

Classification % of students
Distinction 16.3
Merit 19.4
Pass 49
Fail 15.3

Key facts

Department: Statistics

Total students 2016/17: 26

Average class size 2016/17: 26

Controlled access 2016/17: No

Value: Half Unit

Guidelines for interpreting course guide information

Personal development skills

  • Problem solving
  • Application of information skills
  • Communication
  • Application of numeracy skills
  • Commercial awareness
  • Specialist skills