- Teacher: Justine Falciola
- Teacher: Chantal Godel
- Teacher: Mayween Henchoz
- Teacher: Peter Larkin
- Teacher: Michele Pellizzari
- Teacher: Daniel Probst
- Teacher: Sun Vittet
The course aims to provide students with data-based applications of the basic techniques for probabilistic modelling and inference commonly applied in the field of finance in order to analyse data, choose between investments and control market risk.
In the first part of the course we review the fundamentals of statistical inference and estimation, with a focus on linear models. In the second part we introduce the students to basic inferential problems encountered in the time-series analysis of financial data and to their statistical peculiarities. We also introduce applications of the Maximum-Likelihood Estimator to estimate dynamic volatility models that are useful to assess market risk. The last part presents a stochastic structural model of commodity prices and introduces the students to reduced form ARMA modelling and the Box-Jenkins procedure.