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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)

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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://129.3.20.41/eps/ge/papers/0510/0510011.pdf
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Publisher 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
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Handle: RePEc:wpa:wuwpge:0510011

Note: Type of Document - pdf; pages: 18
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Web page: http://129.3.20.41

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

Find related papers by JEL classification:
C61 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Optimization Techniques; Programming Models; Dynamic Analysis
C63 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Computational Techniques
D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  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. [Downloadable!] (restricted)
  2. A. Ruszczynski, 1994. "On Augmented Lagrangian Decomposition Methods For Multistage Stochastic Programs," Working Papers wp94005, International Institute for Applied Systems Analysis. [Downloadable!]
    Other versions:
  3. Diana Barro & Elio Canestrelli, 2005. "Tracking Error: a multistage portfolio model," GE, Growth, Math methods 0510012, EconWPA. [Downloadable!]
  4. 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. [Downloadable!] (restricted)
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