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Prof Clifford Lam (COL 6.09)

This course is compulsory on the BSc in Financial Mathematics and Statistics. This course is available on the BSc in Actuarial Science, BSc in Data Science, BSc in Mathematics with Data 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.

Either ST202, or ST206 and ST211.

Previous programming experience is not required but students who have no previous experience in R must complete an online R pre-sessional course from the Digital Skills Lab before the start of the course (https://moodle.lse.ac.uk/course/view.php?id=7745)

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. Basic time series analysis will be introduced at the start. 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.

This course will be delivered through a combination of classes, lectures and Q&A sessions totalling a minimum of 30 hours across Autumn Term. This course includes a reading week in Week 6 of Autumn Term.

Students will be expected to produce 9 problem sets in the AT.

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.

Exam (80%, duration: 2 hours) in the spring exam period.
Coursework (20%) in the AT.

The course will be assessed by an examination (80%) and a coursework (20%) involving case studies which will be submitted in AT.