A SimILS-based methodology for a portfolio optimization problem with stochastic returns
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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.
| Original language | English |
|---|---|
| Title of host publication | Modeling and Simulation in Engineering, Economics and Management |
| Subtitle of host publication | International Conference, MS 2016, Teruel, Spain, July 4-5, 2016, Proceedings |
| Publisher | Springer International Publishing |
| Pages | 3-11 |
| Number of pages | 9 |
| Volume | 254 |
| ISBN (Electronic) | 978-3-319-40506-3 |
| ISBN (Print) | 978-3-319-40505-6 |
| DOIs | |
| Publication status | Published - 26 Jun 2016 |
Publication series
| Name | Lecture Notes in Business Information Processing |
|---|---|
| ISSN (Print) | 1865-1348 |
| ISSN (Electronic) | 1865-1356 |
Documents
- KIZYS_2016_cright_MSEEM_A SimILS-Based Methodology for a Portfolio Optimization Problem with Stochastic Returns
Rights statement: The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-40506-3_1
Accepted author manuscript (Post-print), 583 KB, PDF document
Related information
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