ST326      Half Unit
Financial Statistics

This information is for the 2022/23 session.

Teacher responsible

Prof Wai-Fung Lam COL.6.09


This course is compulsory on the BSc in Financial Mathematics and Statistics. This course is available on the BSc in Actuarial Science, BSc in Data Science and BSc in Mathematics, Statistics and Business. This course is available with permission as an outside option to students on other programmes where regulations permit and to General Course students.


Either ST202, or ST206 and ST211.

"Previous programming experience is not required but 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 ("

The equivalent link for Python is:

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, Markowitz portfolio theory and the Capital Asset Pricing Model, concepts and  practices in machine learning as applied in financial forecasting, Value at Risk. Will cover classification techniques using random forests and simple trading strategies if time permits.


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

Formative coursework

Students will be expected to produce 9 problem sets in the MT.

Indicative reading

Lecture notes will be provided

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 (80%, duration: 2 hours) in the summer exam period.
Coursework (20%) in the MT.

The course will be assessed by an examination (80%) and a coursework (20%) involving case studies which will be submitted in MT.

Student performance results

(2019/20 - 2021/22 combined)

Classification % of students
First 32
2:1 29.2
2:2 16.9
Third 9.6
Fail 12.4

Key facts

Department: Statistics

Total students 2021/22: 87

Average class size 2021/22: 45

Capped 2021/22: No

Value: Half Unit

Guidelines for interpreting course guide information

Course selection videos

Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.

Personal development skills

  • Self-management
  • Problem solving
  • Communication
  • Application of numeracy skills
  • Specialist skills