Artificial intelligence combined with nonlinear optimization techniques and their application for yield curve optimization
Roya Soltani Seyed Jafar Sadjadi Mona Rahnama
Journal of Industrial & Management Optimization 2017, 13(4): 1701-1721 doi: 10.3934/jimo.2017014

This study makes use of the artificial intelligence approaches combined with some nonlinear optimization techniques for optimization of a well-known problem in financial engineering called yield curve. Yield curve estimation plays an important role on making strategic investment decisions. In this paper, we use two well-known parsimonious estimation models, Nelson-Siegel and Extended Nelson-Siegel, for the yield curve estimation. The proposed models of this paper are formulated as continuous nonlinear optimization problems. The resulted models are then solved using some nonlinear optimization and meta-heuristic approaches. The optimization techniques include hybrid GPSO parallel trust region-dog leg, Hybrid GPSO parallel trust region-nearly exact, Hybrid GPSO parallel Levenberg-Marquardt and Hybrid genetic electromagnetism like algorithm. The proposed models of this paper are examined using some real-world data from the bank of England and the results are analyzed.

keywords: Artificial intelligence nonlinear programming gradient search methods, meta-heuristic approaches yield curve optimization parsimonious method financial engineering

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