杏吧论坛

Prof Piotr Fryzlewicz

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.

This course has a limited number of places (it is controlled access) and demand is typically very high.

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).

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.

22 hours of lectures and 11 hours of seminars in the AT.

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. Ruppert, Matteson (2015) Statistics and Data Analysis for Financial Engineering

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