This course is a self-contained introduction to probabilistic and statistical methods in risk management. It aims to provide hands-on experience implementing these methods.
The course consists of 36 hours of lectures supported by 18 hours of computer-based practical classes, which will allow course participants to implement the lecture material in R.
This course starts with risk factor models and loss distributions, illustrated via various examples in stock, derivative, and bond portfolios. Notion of coherent risk measures are introduced. Value at risk and its statistical estimation are presented. Multivariate factor models are introduced and analysed: covariance and correlation estimates, multivariate normal distributions and their testing, dimensional reduction techniques. The theory of copulas is introduced: meta distributions, tail dependence, fitting copulas to data, measure of dependence on extreme events. The extreme value theory is briefly introduced at the end.
All theoretical materials will be implemented on R using real financial and insurance data.