ST436      Half Unit
Financial Statistics

This information is for the 2023/24 session.

Teacher responsible

Prof Wai-Fung Lam

Availability

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 not available as an outside option.

This course has a limited number of places (it is controlled access) and demand is typically very high. If you have not completed the pre-requisites for this course (ST425 and ST422), you may be asked to take a test that checks your knowledge of some of the material in these pre-requisites, and your acceptance on the course may depend on your success in this test.

Pre-requisites

Students must have completed Statistical Inference: Principles, Methods and Computation (ST425) and Time Series (ST422).

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.

Teaching

This course will be delivered through a combination of classes and lectures totalling a minimum of 30 hours across Winter Term.

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.

Assessment

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

Student performance results

(2019/20 - 2021/22 combined)

Classification % of students
Distinction 18.6
Merit 21.2
Pass 46.9
Fail 13.3

Key facts

Department: Statistics

Total students 2022/23: 38

Average class size 2022/23: 38

Controlled access 2022/23: Yes

Lecture capture used 2022/23: Yes (LT)

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

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