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The performance of stochastic dynamic and xed mix portfolio models

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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The performance of stochastic dynamic and xed mix portfolio models. / Fleten, Stein-Erik; Hoyland, Kjetil; Wallace, Stein W.
In: European Journal of Operational Research, Vol. 140, No. 1, 01.07.2002, p. 37-49.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Fleten, S-E, Hoyland, K & Wallace, SW 2002, 'The performance of stochastic dynamic and xed mix portfolio models', European Journal of Operational Research, vol. 140, no. 1, pp. 37-49. https://doi.org/10.1016/S0377-2217(01)00195-3

APA

Fleten, S-E., Hoyland, K., & Wallace, S. W. (2002). The performance of stochastic dynamic and xed mix portfolio models. European Journal of Operational Research, 140(1), 37-49. https://doi.org/10.1016/S0377-2217(01)00195-3

Vancouver

Fleten S-E, Hoyland K, Wallace SW. The performance of stochastic dynamic and xed mix portfolio models. European Journal of Operational Research. 2002 Jul 1;140(1):37-49. doi: 10.1016/S0377-2217(01)00195-3

Author

Fleten, Stein-Erik ; Hoyland, Kjetil ; Wallace, Stein W. / The performance of stochastic dynamic and xed mix portfolio models. In: European Journal of Operational Research. 2002 ; Vol. 140, No. 1. pp. 37-49.

Bibtex

@article{4b697c0e16cd46c4ab07bb1cd3a69d84,
title = "The performance of stochastic dynamic and xed mix portfolio models",
abstract = "The purpose of this paper is to demonstrate how to evaluate stochastic programming models, and more specifically to compare two different approaches to asset liability management. The first uses multistage stochastic programming, while the other is a static approach based on the so-called constant rebalancing or fixed mix. Particular attention is paid to the methodology used for the comparison. The two alternatives are tested over a large number of realistic scenarios created by means of simulation. We find that due to the ability of the stochastic programming model to adapt to the information in the scenario tree, it dominates the fixed mix approach.",
keywords = "Simulation, Stochastic programming iability management; Performance measurement; Nonlinear programming, Portfolio selection , Asset liability management , Performance measurement , Nonlinear programming",
author = "Stein-Erik Fleten and Kjetil Hoyland and Wallace, {Stein W}",
year = "2002",
month = jul,
day = "1",
doi = "10.1016/S0377-2217(01)00195-3",
language = "English",
volume = "140",
pages = "37--49",
journal = "European Journal of Operational Research",
issn = "0377-2217",
publisher = "Elsevier Science B.V.",
number = "1",

}

RIS

TY - JOUR

T1 - The performance of stochastic dynamic and xed mix portfolio models

AU - Fleten, Stein-Erik

AU - Hoyland, Kjetil

AU - Wallace, Stein W

PY - 2002/7/1

Y1 - 2002/7/1

N2 - The purpose of this paper is to demonstrate how to evaluate stochastic programming models, and more specifically to compare two different approaches to asset liability management. The first uses multistage stochastic programming, while the other is a static approach based on the so-called constant rebalancing or fixed mix. Particular attention is paid to the methodology used for the comparison. The two alternatives are tested over a large number of realistic scenarios created by means of simulation. We find that due to the ability of the stochastic programming model to adapt to the information in the scenario tree, it dominates the fixed mix approach.

AB - The purpose of this paper is to demonstrate how to evaluate stochastic programming models, and more specifically to compare two different approaches to asset liability management. The first uses multistage stochastic programming, while the other is a static approach based on the so-called constant rebalancing or fixed mix. Particular attention is paid to the methodology used for the comparison. The two alternatives are tested over a large number of realistic scenarios created by means of simulation. We find that due to the ability of the stochastic programming model to adapt to the information in the scenario tree, it dominates the fixed mix approach.

KW - Simulation

KW - Stochastic programming iability management; Performance measurement; Nonlinear programming

KW - Portfolio selection

KW - Asset liability management

KW - Performance measurement

KW - Nonlinear programming

U2 - 10.1016/S0377-2217(01)00195-3

DO - 10.1016/S0377-2217(01)00195-3

M3 - Journal article

VL - 140

SP - 37

EP - 49

JO - European Journal of Operational Research

JF - European Journal of Operational Research

SN - 0377-2217

IS - 1

ER -