Portfolio Rebalancing under Uncertainty Using Meta-heuristic Algorithm
| Autor: | Seyed Omid Mohaddesi, Mostafa Zandieh |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Transaction cost
021103 operations research Computer science 0211 other engineering and technologies Downside risk Uncertainty theory 02 engineering and technology Solver Management Science and Operations Research FOS: Economics and business Portfolio Management (q-fin.PM) Portfolio insurance Constant proportion portfolio insurance Risk Management (q-fin.RM) Genetic algorithm Portfolio Algorithm Quantitative Finance - Portfolio Management Quantitative Finance - Risk Management |
| Popis: | In this paper, we solve portfolio rebalancing problem when security returns are represented by uncertain variables considering transaction costs. The performance of the proposed model is studied using constant-proportion portfolio insurance (CPPI) as rebalancing strategy. Numerical results showed that uncertain parameters and different belief degrees will produce different efficient frontiers, and affect the performance of the proposed model. Moreover, CPPI strategy performs as an insurance mechanism and limits downside risk in bear markets while it allows potential benefit in bull markets. Finally, using a globally optimization solver and genetic algorithm (GA) for solving the model, we concluded that the problem size is an important factor in solving portfolio rebalancing problem with uncertain parameters and to gain better results, it is recommended to use a meta-heuristic algorithm rather than a global solver. 21 pages |
| Databáze: | OpenAIRE |
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