infonet-economy

The solvability of dynamic decision problems suffers from the curse of dimensionality, which limits the planning horizon one can afford for mapping the real problem into a numerically solvable dynamic optimization model. In this note, stochastic multistage programming is applied to dynamic fixed-income portfolio selection. We report how well some fixed income portfolio problems are currently solved with barycentric approximation. In particular, we illustrate how the planning horizon affects the numerical effort required to solve the programs. The computational results serve as a benchmark for decomposition methods of mathematical programming.