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Static and dynamic portfolio allocation with nonstandard utility functions

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  • Antonio Santos

Abstract

This article builds on the mean-variance criterion and the links with the expected utility maximization to define the optimal allocation of portfolios, and extends the results in two ways, first considers tailored made utility functions, which can be non continuous and able to capture possible preferences associated with some portfolio managers. Second, it presents results that relate to static (myopic) portfolio allocation decisions connected to dynamic settings where multi-period allocations are considered and conditions are defined to rebalance the portfolio as new information arrive. The conditions are established for the compatibility of static and dynamic decisions associated with different utility functions. We model agents’ decisions associated with portfolio allocation within the expected utility maximization framework. We expect to link the common paradigm of the mean-variance criterion associated with myopic portfolio allocation problems with a more practical implementation of such decision problems, where non continuous utility functions and multi-period type of decisions can play an important role.

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  • Antonio Santos, 2016. "Static and dynamic portfolio allocation with nonstandard utility functions," EcoMod2016 9375, EcoMod.
    • Handle: RePEc:ekd:009007:9375
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    References listed on IDEAS

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    4. Aharon Ben-Tal & Dimitris Bertsimas & David B. Brown, 2010. "A Soft Robust Model for Optimization Under Ambiguity," Operations Research, INFORMS, vol. 58(4-part-2), pages 1220-1234, August.
    5. Kai Ye & Panos Parpas & Berç Rustem, 2012. "Robust portfolio optimization: a conic programming approach," Computational Optimization and Applications, Springer, vol. 52(2), pages 463-481, June.
    6. Reid, Donald W & Tew, Bernard V, 1986. "Mean-Variance versus Direct Utility Maximization: A Comment," Journal of Finance, American Finance Association, vol. 41(5), pages 1177-1179, December.
    7. Katrin Schöttle & Ralf Werner & Rudi Zagst, 2010. "Comparison and robustification of Bayes and Black-Litterman models," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 71(3), pages 453-475, June.
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    Cited by:

    1. Wang, Chou-Wen & Liu, Kai & Li, Bin & Tan, Ken Seng, 2022. "Portfolio optimization under multivariate affine generalized hyperbolic distributions," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 49-66.

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    Keywords

    Portugal; Agent-based modeling; Optimization models;
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