Scenario tree generation for stochastic programming : cases from finance
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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.
Has partsKaut, Michal; Wallace, Stein W.. Evaluation of scenario-generation methods for stochastic programming. Pacific Journal of Optimizalation. 3(2): 257-271, 2007.
Kaut, Michal; Vladimirou, H.; Wallace, Stein W.; Zenios, S.A.. Stability analysis of portfolio management with conditional value-at-risk. QUANTITATIVE FINANCE. 7(4): 397-409, 2007.
Høyland, K.; Kaut, Michal; Wallace, Stein W.. A heuristic for moment-matching scenario generation. Computational Optimization and Applications (The original publication is available at www.springerlink.com). 24(2-3): 169-185, 2003.
Kaut, Michal; Wallace, Stein W.. Multi-period scenario tree generation using moment-matching: Example from option pricing. .