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Multi-period stochastic portfolio optimization: Block-separable decomposition

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  • N. Edirisinghe
  • E. Patterson

Abstract

We consider a multiperiod stochastic programming recourse model for stock portfolio optimization. The presence of various risk and policy constraints leads to significant period-by-period linkage in the model. Furthermore, the dimensionality of the model is large due to many securities under consideration. We propose exploiting block separable recourse structure as well as methods of inducing such structure within nested L-shaped decomposition. We test the model and solution methodology with a base consisting of the Standard & Poor 100 stocks and experiment with several variants of the block separable technique. These are then compared to the standard nested period-by-period decomposition algorithm. It turns out that for financial optimization models of the kind that are discussed in this paper, significant computational efficiencies can be gained with the proposed methodology. Copyright Springer Science+Business Media, LLC 2007

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  • N. Edirisinghe & E. Patterson, 2007. "Multi-period stochastic portfolio optimization: Block-separable decomposition," Annals of Operations Research, Springer, vol. 152(1), pages 367-394, July.
    • Handle: RePEc:spr:annopr:v:152:y:2007:i:1:p:367-394:10.1007/s10479-006-0129-1
    DOI: 10.1007/s10479-006-0129-1
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    1. R. T. Rockafellar & Roger J.-B. Wets, 1991. "Scenarios and Policy Aggregation in Optimization Under Uncertainty," Mathematics of Operations Research, INFORMS, vol. 16(1), pages 119-147, February.
    2. Ogryczak, Wlodzimierz & Ruszczynski, Andrzej, 1999. "From stochastic dominance to mean-risk models: Semideviations as risk measures," European Journal of Operational Research, Elsevier, vol. 116(1), pages 33-50, July.
    3. Julia L. Higle & Suvrajeet Sen, 1991. "Stochastic Decomposition: An Algorithm for Two-Stage Linear Programs with Recourse," Mathematics of Operations Research, INFORMS, vol. 16(3), pages 650-669, August.
    4. C. Roger Glassey, 1973. "Nested Decomposition and Multi-Stage Linear Programs," Management Science, INFORMS, vol. 20(3), pages 282-292, November.
    5. Konno, Hiroshi & Kobayashi, Katsunari, 1997. "An integrated stock-bond portfolio optimization model," Journal of Economic Dynamics and Control, Elsevier, vol. 21(8-9), pages 1427-1444, June.
    6. Hiroshi Konno & Hiroaki Yamazaki, 1991. "Mean-Absolute Deviation Portfolio Optimization Model and Its Applications to Tokyo Stock Market," Management Science, INFORMS, vol. 37(5), pages 519-531, May.
    7. George B. Dantzig & Philip Wolfe, 1960. "Decomposition Principle for Linear Programs," Operations Research, INFORMS, vol. 8(1), pages 101-111, February.
    8. John M. Mulvey & Hercules Vladimirou, 1992. "Stochastic Network Programming for Financial Planning Problems," Management Science, INFORMS, vol. 38(11), pages 1642-1664, November.
    9. Birge, John R. & Louveaux, Francois V., 1988. "A multicut algorithm for two-stage stochastic linear programs," European Journal of Operational Research, Elsevier, vol. 34(3), pages 384-392, March.
    10. John R. Birge, 1985. "Decomposition and Partitioning Methods for Multistage Stochastic Linear Programs," Operations Research, INFORMS, vol. 33(5), pages 989-1007, October.
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    2. Polak, George G. & Rogers, David F. & Sweeney, Dennis J., 2010. "Risk management strategies via minimax portfolio optimization," European Journal of Operational Research, Elsevier, vol. 207(1), pages 409-419, November.
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    7. N C P Edirisinghe & X Zhang, 2008. "Portfolio selection under DEA-based relative financial strength indicators: case of US industries," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(6), pages 842-856, June.

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