A stochastic programming approach for multi-period portfolio optimization View Full Text


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Article Info

DATE

2009-05

AUTHORS

Alois Geyer, Michael Hanke, Alex Weissensteiner

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. More... »

PAGES

187-208

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10287-008-0089-9

DOI

http://dx.doi.org/10.1007/s10287-008-0089-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1011339017


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