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A stochastic programming approach for multi-period portfolio optimization

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Abstract

This paper extends previous work on the use of stochastic linear programming to solve life-cycle investment problems. We combine the feature of asset return predictability with practically relevant constraints arising in a life-cycle investment context. The objective is to maximize the expected utility of consumption over the lifetime and of bequest at the time of death of the investor. Asset returns and state variables follow a first-order vector auto-regression and the associated uncertainty is described by discrete scenario trees. To deal with the long time intervals involved in life-cycle problems we consider a few short-term decisions (to exploit any short-term return predictability), and incorporate a closed-form solution for the long, subsequent steady-state period to account for end effects.

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Authors and Affiliations

  1. WU Wirtschaftsuniversität Wien and Vienna Graduate School of Finance, Heiligenstädter Straße 46, 1190, Vienna, Austria

    Alois Geyer

  2. Department of Banking and Finance, University of Innsbruck, Universitätsstraße 15, 6020, Innsbruck, Austria

    Michael Hanke & Alex Weissensteiner

Authors
  1. Alois Geyer
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  2. Michael Hanke
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  3. Alex Weissensteiner
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Correspondence to Alex Weissensteiner.

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Geyer, A., Hanke, M. & Weissensteiner, A. A stochastic programming approach for multi-period portfolio optimization. Comput Manag Sci 6, 187–208 (2009). https://doi.org/10.1007/s10287-008-0089-9

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