ST436 Half Unit
Financial Statistics
This information is for the 2025/26 session.
Course Convenor
Prof Piotr Fryzlewicz
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 to students on other programmes. This course uses controlled access as part of the course selection process.
How to apply: This course is compulsory on the MSc in Statistics (Financial Statistics) and the 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.
Students should check that they meet the pre-requisites in the course guide before applying, but do not need to provide a written statement. Providing a statement will not aid a student's chances of being accepted onto a course and statements are not read.
Deadline for application: Due to the nature of the method of application, interested students should apply as soon as possible after the opening selection and no later than 10.00am on Friday 26 September 2025.
Course lecturers will aim to make initial offers to students on LSE For You by Friday 26 September.
For queries contact: Stats-Msc@lse.ac.uk
This course has a limited number of places (it is controlled access) and demand is typically very high.
Requisites
Additional requisites:
Knowledge of statistics up to the level of ST202, or alternatively up to the level of Larry Wasserman's "All of Statistics" textbook (or equivalent).
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: basics of time series analysis, 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. If time permits, the course will end with an extended case study involving making predictions of market movements in a virtual trading environment.
Teaching
22 hours of lectures and 11 hours of seminars in the Autumn Term.
Formative assessment
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. Ruppert, Matteson (2015) Statistics and Data Analysis for Financial Engineering
Assessment
Exam (100%), duration: 120 Minutes in the Spring exam period
Key facts
Department: Statistics
Course Study Period: Autumn Term
Unit value: Half unit
FHEQ Level: Level 7
CEFR Level: Null
Total students 2024/25: 33
Average class size 2024/25: 33
Controlled access 2024/25: YesCourse 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