Not available in 2017/18
ST326      Half Unit
Financial Statistics

This information is for the 2017/18 session.

Teacher responsible

Prof Pauline Barrieu COL.6.03

Availability

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

ST202 Probability, Distribution Theory and Inference.

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, statistics and machine learning in financial forecasting, Value at Risk, simple trading strategies, statistics of fixed income finance, derivative instruments from the statistical viewpoint.

Teaching

20 hours of lectures and 10 hours of seminars in the MT.

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

Key facts

Department: Statistics

Total students 2016/17: Unavailable

Average class size 2016/17: Unavailable

Capped 2016/17: No

Value: Half Unit

Guidelines for interpreting course guide information

PDAM skills

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