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Horizon and stages in applications of stochastic programming in finance

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  • Marida Bertocchi
  • Vittorio Moriggia
  • Jitka Dupačová

Abstract

To solve a decision problem under uncertainty via stochastic programming means to choose or to build a suitable stochastic programming model taking into account the nature of the real-life problem, character of input data, availability of software and computer technology. In applications of multistage stochastic programs additional rather complicated modeling issues come to the fore. They concern the choice of the horizon, stages, methods for generating scenario trees, etc. We shall discuss briefly the ways of selecting horizon and stages in financial applications. In our numerical studies, we focus on alternative choices of stages and their impact on optimal first-stage solutions of bond portfolio optimization problems. Copyright Springer Science + Business Media, Inc. 2006

Suggested Citation

  • Marida Bertocchi & Vittorio Moriggia & Jitka Dupačová, 2006. "Horizon and stages in applications of stochastic programming in finance," Annals of Operations Research, Springer, vol. 142(1), pages 63-78, February.
    • Handle: RePEc:spr:annopr:v:142:y:2006:i:1:p:63-78:10.1007/s10479-006-6161-3
    DOI: 10.1007/s10479-006-6161-3
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    References listed on IDEAS

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    1. Dupacova, Jitka & Bertocchi, Marida, 2001. "From data to model and back to data: A bond portfolio management problem," European Journal of Operational Research, Elsevier, vol. 134(2), pages 261-278, October.
    2. Fleten, Stein-Erik & Hoyland, Kjetil & Wallace, Stein W., 2002. "The performance of stochastic dynamic and fixed mix portfolio models," European Journal of Operational Research, Elsevier, vol. 140(1), pages 37-49, July.
    3. Zenios, Stavros A. & Holmer, Martin R. & McKendall, Raymond & Vassiadou-Zeniou, Christiana, 1998. "Dynamic models for fixed-income portfolio management under uncertainty," Journal of Economic Dynamics and Control, Elsevier, vol. 22(10), pages 1517-1541, August.
    4. Jitka Dupačová & Giorgio Consigli & Stein Wallace, 2000. "Scenarios for Multistage Stochastic Programs," Annals of Operations Research, Springer, vol. 100(1), pages 25-53, December.
    5. Marida Bertocchi & Vittorio Moriggia & Jitka Dupačová, 2000. "Sensitivity of Bond Portfolio's Behavior with Respect to Random Movements in Yield Curve: A Simulation Study," Annals of Operations Research, Springer, vol. 99(1), pages 267-286, December.
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    3. Kallio, Markku & Halme, Merja & Dehghan Hardoroudi, Nasim & Aspara, Jaakko, 2022. "Transparent structured products for retail investors," European Journal of Operational Research, Elsevier, vol. 302(2), pages 752-767.
    4. Lijian Chen & Tito Homem-de-Mello, 2010. "Re-solving stochastic programming models for airline revenue management," Annals of Operations Research, Springer, vol. 177(1), pages 91-114, June.
    5. Robert Ferstl & Alex Weissensteiner, 2010. "Cash management using multi-stage stochastic programming," Quantitative Finance, Taylor & Francis Journals, vol. 10(2), pages 209-219.
    6. Weissensteiner, Alex, 2010. "Using the Black-Derman-Toy interest rate model for portfolio optimization," European Journal of Operational Research, Elsevier, vol. 202(1), pages 175-181, April.
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    9. Robert Ferstl & Alex Weissensteiner, 2010. "Backtesting short-term treasury management strategies based on multi-stage stochastic programming," Journal of Asset Management, Palgrave Macmillan, vol. 11(2), pages 94-112, June.

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