ST326      Half Unit
Financial Statistics

This information is for the 2021/22 session.

Teacher responsible

Prof Wai-Fung Lam COL.6.09

Availability

This course is compulsory on the BSc in Financial Mathematics and Statistics. This course is available on the BSc in Actuarial 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.

Pre-requisites

Either ST202, or ST206 and ST211.

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.

Teaching

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 year, some of this teaching may be delivered through a combination of classes and flipped-lectures delivered as short online videos. 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.

Assessment

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.

Key facts

Department: Statistics

Total students 2020/21: 60

Average class size 2020/21: 28

Capped 2020/21: No

Value: Half Unit

Guidelines for interpreting course guide information

Personal development skills

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