Quantitative Stability in Stochastic Programming: The Method of Probability Metrics
Mathematics of Operations Research
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
Generating Scenario Trees for Multistage Decision Problems
Management Science
Epi-convergent discretizations of stochastic programs via integration quadratures
Numerische Mathematik
Variance reduction in sample approximations of stochastic programs
Mathematical Programming: Series A and B
Polyhedral Risk Measures in Stochastic Programming
SIAM Journal on Optimization
Applications of Stochastic Programming (Mps-Siam Series on Optimization) (Mps-Saimseries on Optimization)
Stability of Multistage Stochastic Programs
SIAM Journal on Optimization
Natural Computing in Computational Finance (Studies in Computational Intelligence)
Natural Computing in Computational Finance (Studies in Computational Intelligence)
Epi-convergent discretizations of multistage stochastic programs via integration quadratures
Mathematical Programming: Series A and B - Nonlinear convex optimization and variational inequalities
An Introduction to Natural Computing in Finance
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Natural Computing in Computational Finance: Volume 2
Natural Computing in Computational Finance: Volume 2
Algorithmic Aspects of Scenario-Based Multi-stage Decision Process Optimization
ADT '09 Proceedings of the 1st International Conference on Algorithmic Decision Theory
An Introduction to Evolutionary Computation in Finance
IEEE Computational Intelligence Magazine
| Hi-index | 0.00 |
Multi-stage financial decision optimization under uncertainty depends on a careful numerical approximation of the underlying stochastic process, which describes the future returns of the selected assets or asset categories. Various approaches towards an optimal generation of discrete-time, discrete-state approximations (represented as scenario trees) have been suggested in the literature. In this paper, a new evolutionary algorithm to create scenario trees for multi-stage financial optimization models will be presented. Numerical results and implementation details conclude the paper.