A mixed integer programming model for multistage mean–variance post-tax optimization

In this paper, we introduce a mixed integer stochastic programming approach to mean–variance post-tax portfolio management. This approach takes into account of risk in a multistage setting and allows general withdrawals from original capital. The uncertainty on asset returns is specified as a scenar...

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Vydané v:European journal of operational research Ročník 185; číslo 2; s. 451 - 480
Hlavní autori: Osorio, Maria A., Gulpinar, Nalan, Rustem, Berc
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Amsterdam Elsevier B.V 01.03.2008
Elsevier
Elsevier Sequoia S.A
Edícia:European Journal of Operational Research
Predmet:
Applied sciences
Exact sciences and technology
Integer programming
Mathematical programming
Mean–variance portfolio management
Multistage stochastic mixed integer quadratic programming
Operational research and scientific management
Operational research. Management science
Optimization
Portfolio management
Portfolio theory
Post-tax optimization
Quadratic programming
Risk
Risk theory. Actuarial science
Stochastic models
Studies
Mean–variance portfolio management
Multistage stochastic mixed integer quadratic programming
Post-tax optimization
Script
Mean-variance portfolio management
Probabilistic approach
Mixed integer programming
Tax
Capital
Diversification
Modeling
Optimization
Stochastic programming
Risk aversion
Uncertain system
Stock exchange
Robustness
Risk management
Portfolio management
ISSN:0377-2217, 1872-6860
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Popis
Shrnutí:In this paper, we introduce a mixed integer stochastic programming approach to mean–variance post-tax portfolio management. This approach takes into account of risk in a multistage setting and allows general withdrawals from original capital. The uncertainty on asset returns is specified as a scenario tree. The risk across scenarios is addressed using the probabilistic approach of classical stochastic programming. The tax rules are used with stochastic linear and mixed integer quadratic programming models to compute an overall tax and return-risk efficient multistage portfolio. The incorporation of the risk term in the model provides robustness and leads to diversification over wrappers and assets within each wrapper. General withdrawals and risk aversion have an impact on the distribution of assets among wrappers. Computational results are presented using a study with different scenario trees in order to show the performance of these models.
Bibliografia:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2006.09.105