杏吧论坛

Dr Tengyao Wang

This course is compulsory on the MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (杏吧论坛 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.

This course has a limited number of places (it is controlled access) and demand is typically very high. If you have not completed the pre-requisites for this course (ST425 and ST422), you may be asked to take a test that checks your knowledge of some of the material in these pre-requisites, and your acceptance on the course may depend on your success in this test.

Students must have completed Statistical Inference: Principles, Methods and Computation (ST425) and Time Series (ST422).

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.

Weekly marked problem sheets, with solutions discussed in class. Two marked case studies.

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.