Scenario tree generation for stochastic programming : cases from finance

In recent years, stochastic programming has gained an increasing popularity within the mathematical programming community, mainly because the present computing power allows users to add stochasticity to models that were difficult to solve in deterministic versions only a few years ago. For general information about stochastic programming, see for example Dantzig (1955); Birge and Louveaux (1997), or Kall and Wallace (1994). As a result, a lot of research has been done on various aspects of stochastic programming. However, scenario generation has remained out of the main field of interest. In this thesis, we try to explain the importance of scenario generation for stochastic programming, as well as provide some methods for both generating the scenarios and testing their quality