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Time and nodal decomposition with implicit non-anticipativity constraints in dynamic portfolio optimization

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Author Info

  • Diana Barro

    (Department of Applied Mathematics - University of Venice)

  • Elio Canestrelli

    (Department of Applied Mathematics - University of Venice)

Abstract

We propose a decomposition method for the solution of a dynamic portfolio optimization problem which fits the formulation of a multistage stochastic programming problem. The method allows to obtain time and nodal decomposition of the problem in its arborescent formulation applying a discrete version of Pontryagin Maximum Principle. The solution of the decomposed problems is coordinated through a fixed- point weighted iterative scheme. The introduction of an optimization step in the choice of the weights at each iteration allows to solve the original problem in a very efficient way.

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File URL: http://128.118.178.162/eps/ge/papers/0510/0510011.pdf
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Bibliographic Info

Paper provided by EconWPA in its series GE, Growth, Math methods with number 0510011.

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Length: 18 pages
Date of creation: 28 Oct 2005
Date of revision:
Handle: RePEc:wpa:wuwpge:0510011

Note: Type of Document - pdf; pages: 18
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Web page: http://128.118.178.162

Related research

Keywords: Stochastic programming; Discrete time optimal control problem; Iterative scheme; Portfolio optimization;

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References

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  1. Barro, Diana & Canestrelli, Elio, 2005. "Dynamic portfolio optimization: Time decomposition using the Maximum Principle with a scenario approach," European Journal of Operational Research, Elsevier, vol. 163(1), pages 217-229, May.
  2. Vladimirou, Hercules, 1998. "Computational assessment of distributed decomposition methods for stochastic linear programs," European Journal of Operational Research, Elsevier, vol. 108(3), pages 653-670, August.
  3. Diana Barro & Elio Canestrelli, 2005. "Tracking Error: a multistage portfolio model," GE, Growth, Math methods 0510012, EconWPA.
  4. John R. Birge & Liqun Qi, 1988. "Computing Block-Angular Karmarkar Projections with Applications to Stochastic Programming," Management Science, INFORMS, vol. 34(12), pages 1472-1479, December.
  5. C.H. Rosa & A. Ruszczynski, 1994. "On Augmented Lagrangian Decomposition Methods for Multistage Stochastic Programs," Working Papers wp94125, International Institute for Applied Systems Analysis.
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Cited by:
  1. Diana Barro & Elio Canestrelli, 2011. "Combining stochastic programming and optimal control to solve multistage stochastic optimization problems," Working Papers 2011_24, Department of Economics, University of Venice "Ca' Foscari", revised 2011.

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