ST436      Half Unit
Financial Statistics

This information is for the 2020/21 session.

Teacher responsible

Prof Piotr Fryzlewicz COL 5.12


This course is compulsory on the MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (LSE and Fudan) 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.


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.


This course will be delivered through a combination of classes and lectures totalling a minimum of 30 hours across Lent Term. This year, some or all of this teaching may be delivered through a combination of virtual classes and flipped lectures delivered as short online videos.


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 summer exam period.

Student performance results

(2016/17 - 2018/19 combined)

Classification % of students
Distinction 9.4
Merit 31.2
Pass 41.7
Fail 17.7

Important information in response to COVID-19

Please note that during 2020/21 academic year some variation to teaching and learning activities may be required to respond to changes in public health advice and/or to account for the situation of students in attendance on campus and those studying online during the early part of the academic year. For assessment, this may involve changes to mode of delivery and/or the format or weighting of assessments. Changes will only be made if required and students will be notified about any changes to teaching or assessment plans at the earliest opportunity.

Key facts

Department: Statistics

Total students 2019/20: 29

Average class size 2019/20: 29

Controlled access 2019/20: Yes

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