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Differential Evolution (DEoptim) for Non-Convex Portfolio Optimization

Author

Listed:
  • Ardia, David
  • Boudt, Kris
  • Carl, Peter
  • Mullen, Katharine M.
  • Peterson, Brian

Abstract

The R package DEoptim implements the differential evolution algorithm. This algorithm is an evolutionary technique similar to genetic algorithms that is useful for the solution of global optimization problems. In this note we provide an introduction to the package and demonstrate its utility for financial applications by solving a non-convex portfolio optimization problem.

Suggested Citation

  • Ardia, David & Boudt, Kris & Carl, Peter & Mullen, Katharine M. & Peterson, Brian, 2010. "Differential Evolution (DEoptim) for Non-Convex Portfolio Optimization," MPRA Paper 22135, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:22135
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    File URL: https://mpra.ub.uni-muenchen.de/28187/1/MPRA_paper_28187.pdf
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    References listed on IDEAS

    as
    1. Mullen, Katharine M. & Ardia, David & Gil, David L. & Windover, Donald & Cline, James, 2011. "DEoptim: An R Package for Global Optimization by Differential Evolution," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i06).
    2. Manfred Gilli & Enrico Schumann, 2012. "Heuristic optimisation in financial modelling," Annals of Operations Research, Springer, vol. 193(1), pages 129-158, March.
    3. Jan Börner & Steven I. Higgins & Jochen Kantelhardt & Simon Scheiter, 2007. "Rainfall or price variability: what determines rangeland management decisions? A simulation‐optimization approach to South African savannas," Agricultural Economics, International Association of Agricultural Economists, vol. 37(2‐3), pages 189-200, September.
    4. O. Scaillet, 2004. "Nonparametric Estimation and Sensitivity Analysis of Expected Shortfall," Mathematical Finance, Wiley Blackwell, vol. 14(1), pages 115-129, January.
    5. Maringer Dietmar G. & Meyer Mark, 2008. "Smooth Transition Autoregressive Models -- New Approaches to the Model Selection Problem," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 12(1), pages 1-21, March.
    6. Higgins, Steven I. & Kantelhardt, Jochen & Scheiter, Simon & Boerner, Jan, 2007. "Sustainable management of extensively managed savanna rangelands," Ecological Economics, Elsevier, vol. 62(1), pages 102-114, April.
    7. Thiemo Krink & Sandra Paterlini, 2011. "Multiobjective optimization using differential evolution for real-world portfolio optimization," Computational Management Science, Springer, vol. 8(1), pages 157-179, April.
    8. Manfred Gilli & Dietmar Maringer & Peter Winker, 2008. "Applications of Heuristics in Finance," International Handbooks on Information Systems, in: Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), Handbook on Information Technology in Finance, chapter 26, pages 635-653, Springer.
    9. Manfred Gilli & Peter Winker, 2008. "Review of Heuristic Optimization Methods in Econometrics," Working Papers 001, COMISEF.
    10. Thiemo Krink & Stefan Mittnik & Sandra Paterlini, 2009. "Differential evolution and combinatorial search for constrained index-tracking," Annals of Operations Research, Springer, vol. 172(1), pages 153-176, November.
    11. Thiemo Krink & Sandra Paterlini, 2008. "Differential Evolution for Multiobjective Portfolio Optimization," Center for Economic Research (RECent) 021, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
    12. Thiemo Krink & Stefan Mittnik & Sandra Paterlini, 2009. "Differential evolution and combinatorial search for constrained index-tracking," Annals of Operations Research, Springer, vol. 172(1), pages 153-176, November.
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    Citations

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    Cited by:

    1. Mullen, Katharine M. & Ardia, David & Gil, David L. & Windover, Donald & Cline, James, 2011. "DEoptim: An R Package for Global Optimization by Differential Evolution," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i06).
    2. Ankit Dangi, 2013. "Financial Portfolio Optimization: Computationally guided agents to investigate, analyse and invest!?," Papers 1301.4194, arXiv.org.
    3. Ryś Przemysław & Ślepaczuk Robert, 2018. "Machine Learning Methods in Algorithmic Trading Strategy Optimization – Design and Time Efficiency," Central European Economic Journal, Sciendo, vol. 5(52), pages 206-229, January.
    4. Bilel JARRAYA, 2013. "Asset Allocation And Portfolio Optimization Problems With Metaheuristics: A Literature Survey," Business Excellence and Management, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 3(4), pages 38-56, December.
    5. Przemysław Ryś & Robert Ślepaczuk, 2018. "Machine learning in algorithmic trading strategy optimization - implementation and efficiency," Working Papers 2018-25, Faculty of Economic Sciences, University of Warsaw.
    6. Ardia, David & Ospina, Juan & Giraldo, Giraldo, 2010. "Jump-Diffusion Calibration using Differential Evolution," MPRA Paper 26184, University Library of Munich, Germany, revised 25 Oct 2010.

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    More about this item

    Keywords

    Differential optimization; non-convex portfolio optimization; DEoptim; R software;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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