A SimILS-based methodology for a portfolio optimization problem with stochastic returns
A SimILS-based methodology for a portfolio optimization problem with stochastic returns
Combinatorial optimization has been at the heart of financial and risk management. This body of research is dominated by the mean-variance efficient frontier (MVEF) that solves the portfolio optimization problem (POP), pioneered by Harry Markowitz. The classical version of the POP minimizes risk for a given expected return on a portfolio of assets by setting the weights of those assets. Most authors deal with the variability of returns and covariances by employing expected values. In contrast, we propose a simheuristic methodology (combining the simulated annealing metaheuristic with Monte Carlo simulation), in which returns and covariances are modeled as random variables following specific probability distributions. Our methodology assumes that the best solution for a scenario with constant expected values may have poor performance in a dynamic world. A computational experiment is carried out to illustrate our approach.
portfolio optimization, SimILS, metaheuristics, simulation
3-11
Calvet, Laura
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Kizys, Renatas
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Juan, Angel A.
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De Armas, Jesica
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Calvet, Laura
0c8e51bc-5ec3-469b-a8ab-cb2b1c760c33
Kizys, Renatas
9d3a6c5f-075a-44f9-a1de-32315b821978
Juan, Angel A.
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De Armas, Jesica
4f636d1a-c3c3-480e-8127-4f659308b706
Calvet, Laura, Kizys, Renatas, Juan, Angel A. and De Armas, Jesica
(2016)
A SimILS-based methodology for a portfolio optimization problem with stochastic returns.
León, R., Muñoz-Torres, M. and Moneva, J.
(eds.)
In Modeling and Simulation in Engineering, Economics and Management. MS 2016.
vol. 254,
Springer.
.
(doi:10.1007/978-3-319-40506-3_1).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Combinatorial optimization has been at the heart of financial and risk management. This body of research is dominated by the mean-variance efficient frontier (MVEF) that solves the portfolio optimization problem (POP), pioneered by Harry Markowitz. The classical version of the POP minimizes risk for a given expected return on a portfolio of assets by setting the weights of those assets. Most authors deal with the variability of returns and covariances by employing expected values. In contrast, we propose a simheuristic methodology (combining the simulated annealing metaheuristic with Monte Carlo simulation), in which returns and covariances are modeled as random variables following specific probability distributions. Our methodology assumes that the best solution for a scenario with constant expected values may have poor performance in a dynamic world. A computational experiment is carried out to illustrate our approach.
Text
KIZYS 2016 cright MSEEM A SimILS-Based Methodology for a Portfolio Optimization Problem with Stochastic Returns
- Accepted Manuscript
More information
e-pub ahead of print date: 26 June 2016
Keywords:
portfolio optimization, SimILS, metaheuristics, simulation
Identifiers
Local EPrints ID: 434501
URI: http://eprints.soton.ac.uk/id/eprint/434501
PURE UUID: e33b66cd-ba7f-4228-8270-04ade537d98a
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Date deposited: 25 Sep 2019 16:30
Last modified: 16 Mar 2024 04:41
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Contributors
Author:
Laura Calvet
Author:
Angel A. Juan
Author:
Jesica De Armas
Editor:
R. León
Editor:
M. Muñoz-Torres
Editor:
J. Moneva
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