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Pesquisa Operacional

versão impressa ISSN 0101-7438versão On-line ISSN 1678-5142

Resumo

OLIVEIRA, Alan Delgado de; FILOMENA, Tiago Pascoal  e  RIGHI, Marcelo Brutti. PERFORMANCE COMPARISON OF SCENARIO-GENERATION METHODS APPLIED TO A STOCHASTIC OPTIMIZATION ASSET-LIABILITY MANAGEMENT MODEL. Pesqui. Oper. [online]. 2018, vol.38, n.1, pp.53-72. ISSN 0101-7438.  https://doi.org/10.1590/0101-7438.2018.038.01.0053.

In this paper, we provide an empirical discussion of the differences among some scenario tree-generation approaches for stochastic programming. We consider the classical Monte Carlo sampling and Moment matching methods. Moreover, we test the Resampled average approximation, which is an adaptation of Monte Carlo sampling and Monte Carlo with naive allocation strategy as the benchmark. We test the empirical effects of each approach on the stability of the problem objective function and initial portfolio allocation, using a multistage stochastic chance-constrained asset-liability management (ALM) model as the application. The Moment matching and Resampled average approximation are more stable than the other two strategies.

Palavras-chave : scenario generation; stochastic programing; multistage; ALM.

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