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Scenario optimization asset and liability modelling for individual investors

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  • Andrea Consiglio
  • Flavio Cocco
  • Stavros Zenios

Abstract

We develop a scenario optimization model for asset and liability management of individual investors. The individual has a given level of initial wealth and a target goal to be reached within some time horizon. The individual must determine an asset allocation strategy so that the portfolio growth rate will be sufficient to reach the target. A scenario optimization model is formulated which maximizes the upside potential of the portfolio, with limits on the downside risk. Both upside and downside are measured vis-à-vis the goal. The stochastic behavior of asset returns is captured through bootstrap simulation, and the simulation is embedded in the model to determine the optimal portfolio. Post-optimality analysis using out-of-sample scenarios measures the probability of success of a given portfolio. It also allows us to estimate the required increase in the initial endowment so that the probability of success is improved. Copyright Springer Science+Business Media, LLC 2007

Suggested Citation

  • Andrea Consiglio & Flavio Cocco & Stavros Zenios, 2007. "Scenario optimization asset and liability modelling for individual investors," Annals of Operations Research, Springer, vol. 152(1), pages 167-191, July.
    • Handle: RePEc:spr:annopr:v:152:y:2007:i:1:p:167-191:10.1007/s10479-006-0133-5
    DOI: 10.1007/s10479-006-0133-5
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    Cited by:

    1. Jang Ho Kim & Yongjae Lee & Woo Chang Kim & Frank J. Fabozzi, 2022. "Goal-based investing based on multi-stage robust portfolio optimization," Annals of Operations Research, Springer, vol. 313(2), pages 1141-1158, June.
    2. Audrius Kabašinskas & Francesca Maggioni & Kristina Šutienė & Eimutis Valakevičius, 2019. "A multistage risk-averse stochastic programming model for personal savings accrual: the evidence from Lithuania," Annals of Operations Research, Springer, vol. 279(1), pages 43-70, August.
    3. Pagnoncelli, Bernardo K. & Homem-de-Mello, Tito & Lagos, Guido & Castañeda, Pablo & García, Javier, 2024. "Solving constrained consumption–investment problems by decomposition algorithms," European Journal of Operational Research, Elsevier, vol. 319(1), pages 292-302.
    4. Jang Ho Kim & Woo Chang Kim & Frank J. Fabozzi, 2021. "Sparse factor model based on trend filtering," Annals of Operations Research, Springer, vol. 306(1), pages 321-342, November.
    5. Miloš Kopa & Vittorio Moriggia & Sebastiano Vitali, 2018. "Individual optimal pension allocation under stochastic dominance constraints," Annals of Operations Research, Springer, vol. 260(1), pages 255-291, January.
    6. Anne Pedersen & Alex Weissensteiner & Rolf Poulsen, 2013. "Financial planning for young households," Annals of Operations Research, Springer, vol. 205(1), pages 55-76, May.
    7. Sebastiano Vitali & Vittorio Moriggia & Miloš Kopa, 2017. "Optimal pension fund composition for an Italian private pension plan sponsor," Computational Management Science, Springer, vol. 14(1), pages 135-160, January.
    8. Das, Sanjiv R. & Ostrov, Daniel & Radhakrishnan, Anand & Srivastav, Deep, 2022. "Dynamic optimization for multi-goals wealth management," Journal of Banking & Finance, Elsevier, vol. 140(C).
    9. Andrea Consiglio & Flavio Cocco & Stavros A. Zenios, 2001. "The Value of Integrative Risk Management for Insurance Products with Guarantees," Journal of Risk Finance, Emerald Group Publishing Limited, vol. 2(3), pages 6-16, February.
    10. Andrea Consiglio & Flavio Cocco & Stavros A. Zenios, 2004. "www.Personal_Asset_Allocation," Interfaces, INFORMS, vol. 34(4), pages 287-302, August.

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