IDEAS home Printed from https://ideas.repec.org/a/gam/jijfss/v10y2022i1p20-d767153.html
   My bibliography  Save this article

Efficient Asset Allocation: Application of Game Theory-Based Model for Superior Performance

Author

Listed:
  • Mirza Sikalo

    (School of Economics and Business, University of Sarajevo, Trg Oslobodjenja—Alija Izetbegovic 1, 71000 Sarajevo, Bosnia and Herzegovina)

  • Almira Arnaut-Berilo

    (School of Economics and Business, University of Sarajevo, Trg Oslobodjenja—Alija Izetbegovic 1, 71000 Sarajevo, Bosnia and Herzegovina)

  • Azra Zaimovic

    (School of Economics and Business, University of Sarajevo, Trg Oslobodjenja—Alija Izetbegovic 1, 71000 Sarajevo, Bosnia and Herzegovina)

Abstract

In this paper, we compared the models for selecting the optimal portfolio based on different risk measures to identify the periods in which some of the risk measures dominated over others. For decades, the best known return-risk model has been Markowitz’s mean-variance model. Based on the criticism of the classical Markowitz model, a whole series of risk measures and models for selecting the optimal portfolio have been developed, which are divided into two groups: symmetrical and downside risk measures. Based on the tools provided by game theory, we presented a minimax model for selecting the optimal portfolio based on the maximum loss as a measure of risk. Recent research has shown the adequacy of the application of this risk measure and its dominance concerning variance in certain circumstances. Theoretically, the model based on maximum loss as a measure of risk relies on a much smaller number of assumptions that must be satisfied. In the empirical part of the paper, we analyzed the real return performance, structure, correlation, stability, and predictive efficiency of the model based on maximum loss return as a measure of risk and compared it with the other famous models to determine whether the maximum loss-based risk measure model is more suitable for use in certain circumstances than conventional return-risk models. We compared portfolios created based on different models over the period of 2000–2020 from a selected sample of stocks that are components of the STOXX Europe 600 index, which covers 90% of the free market capitalization in the European capital market. The observed period included 3 bear market periods, including the period of market decline during the COVID-19 crisis. Our analysis showed that there was no significant difference in portfolio returns depending on the selected model using the “buy-and-hold” strategy, but there were crisis periods. The results showed a significantly higher stability of portfolios selected on the criterion of minimizing the maximum loss than others. In periods of market decline, this portfolio achieved the best performance and had a shorter recovery period than others. This allowed superior use of the minimax model at least for investors with a pronounced risk aversion.

Suggested Citation

  • Mirza Sikalo & Almira Arnaut-Berilo & Azra Zaimovic, 2022. "Efficient Asset Allocation: Application of Game Theory-Based Model for Superior Performance," IJFS, MDPI, vol. 10(1), pages 1-15, March.
    • Handle: RePEc:gam:jijfss:v:10:y:2022:i:1:p:20-:d:767153
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7072/10/1/20/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7072/10/1/20/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Amita Sharma & Sebastian Utz & Aparna Mehra, 2017. "Omega-CVaR portfolio optimization and its worst case analysis," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(2), pages 505-539, March.
    2. Blattberg, Robert C & Gonedes, Nicholas J, 1974. "A Comparison of the Stable and Student Distributions as Statistical Models for Stock Prices," The Journal of Business, University of Chicago Press, vol. 47(2), pages 244-280, April.
    3. Krokhmal, Pavlo & Rockafellar, R. Tyrrell & Uryasev, Stan, 2006. "Risk management and optimization in finance," Journal of Banking & Finance, Elsevier, vol. 30(2), pages 315-315, February.
    4. Peter Byrne & Stephen Lee, 2004. "Different Risk Measures: Different Portfolio Compositions?," Real Estate & Planning Working Papers rep-wp2004-03, Henley Business School, University of Reading.
    5. Shehzad, Khurram & Xiaoxing, Liu & Kazouz, Hayfa, 2020. "COVID-19’s disasters are perilous than Global Financial Crisis: A rumor or fact?," Finance Research Letters, Elsevier, vol. 36(C).
    6. Righi, Marcelo Brutti & Borenstein, Denis, 2018. "A simulation comparison of risk measures for portfolio optimization," Finance Research Letters, Elsevier, vol. 24(C), pages 105-112.
    7. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    8. Peter Byrne & Stephen Lee, 2004. "Different Risk Measures: Different Portfolio Compositions?," ERES eres2004_516, European Real Estate Society (ERES).
    9. Azra Zaimovic & Adna Omanovic & Almira Arnaut-Berilo, 2021. "How Many Stocks Are Sufficient for Equity Portfolio Diversification? A Review of the Literature," JRFM, MDPI, vol. 14(11), pages 1-30, November.
    10. Sowmya Subramaniam & Madhumita Chakraborty, 2021. "COVID-19 fear index: does it matter for stock market returns?," Review of Behavioral Finance, Emerald Group Publishing Limited, vol. 13(1), pages 40-50, March.
    11. Martin R. Young, 1998. "A Minimax Portfolio Selection Rule with Linear Programming Solution," Management Science, INFORMS, vol. 44(5), pages 673-683, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mirza Sikalo & Almira Arnaut-Berilo & Adela Delalic, 2023. "A Combined AHP-PROMETHEE Approach for Portfolio Performance Comparison," IJFS, MDPI, vol. 11(1), pages 1-15, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sabastine Mushori & Delson Chikobvu, 2016. "A Stochastic Multi-stage Trading Cost model in optimal portfolio selection," EERI Research Paper Series EERI RP 2016/23, Economics and Econometrics Research Institute (EERI), Brussels.
    2. Mirza Sikalo & Almira Arnaut-Berilo & Adela Delalic, 2023. "A Combined AHP-PROMETHEE Approach for Portfolio Performance Comparison," IJFS, MDPI, vol. 11(1), pages 1-15, March.
    3. Ahmed Imran Hunjra & Suha Mahmoud Alawi & Sisira Colombage & Uroosa Sahito & Mahnoor Hanif, 2020. "Portfolio Construction by Using Different Risk Models: A Comparison among Diverse Economic Scenarios," Risks, MDPI, vol. 8(4), pages 1-23, November.
    4. P. Bonami & M. A. Lejeune, 2009. "An Exact Solution Approach for Portfolio Optimization Problems Under Stochastic and Integer Constraints," Operations Research, INFORMS, vol. 57(3), pages 650-670, June.
    5. Carsten Lausberg & Stephen Lee & Moritz Müller & Cay Oertel & Tobias Schultheiß, 2020. "Risk measures for direct real estate investments with non-normal or unknown return distributions," Zeitschrift für Immobilienökonomie (German Journal of Real Estate Research), Springer;Gesellschaft für Immobilienwirtschaftliche Forschung e. V., vol. 6(1), pages 3-27, April.
    6. Gianfranco Guastaroba & Renata Mansini & Wlodzimierz Ogryczak & M. Grazia Speranza, 2020. "Enhanced index tracking with CVaR-based ratio measures," Annals of Operations Research, Springer, vol. 292(2), pages 883-931, September.
    7. Hoe, Lam Weng & Saiful Hafizah, Jaaman & Zaidi, Isa, 2010. "An empirical comparison of different risk measures in portfolio optimization," Business and Economic Horizons (BEH), Prague Development Center (PRADEC), vol. 1(1), pages 1-7, April.
    8. Alessia Naccarato & Andrea Pierini & Giovanna Ferraro, 2021. "Markowitz portfolio optimization through pairs trading cointegrated strategy in long-term investment," Annals of Operations Research, Springer, vol. 299(1), pages 81-99, April.
    9. Malavasi, Matteo & Ortobelli Lozza, Sergio & Trück, Stefan, 2021. "Second order of stochastic dominance efficiency vs mean variance efficiency," European Journal of Operational Research, Elsevier, vol. 290(3), pages 1192-1206.
    10. José Santiago Fajardo Barbachan & Aquiles Rocha de Farias & José Renato Haas Ornelas, 2008. "A Goodness-of-Fit Test with Focus on Conditional Value at Risk," Brazilian Review of Finance, Brazilian Society of Finance, vol. 6(2), pages 139-155.
    11. Massimiliano Caporin & Grégory M. Jannin & Francesco Lisi & Bertrand B. Maillet, 2014. "A Survey On The Four Families Of Performance Measures," Journal of Economic Surveys, Wiley Blackwell, vol. 28(5), pages 917-942, December.
    12. Maziar Sahamkhadam, 2021. "Dynamic copula-based expectile portfolios," Journal of Asset Management, Palgrave Macmillan, vol. 22(3), pages 209-223, May.
    13. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
    14. Ibragimov, Rustam & Walden, Johan, 2007. "The limits of diversification when losses may be large," Scholarly Articles 2624460, Harvard University Department of Economics.
    15. Kristin Wellner, 2011. "Transforming Markowitz portfolio theory into a practical real estate portfolio allocation process," ERES eres2011_341, European Real Estate Society (ERES).
    16. Istvan Varga-Haszonits & Fabio Caccioli & Imre Kondor, 2016. "Replica approach to mean-variance portfolio optimization," Papers 1606.08679, arXiv.org.
    17. Weron, Rafał, 2004. "Computationally intensive Value at Risk calculations," Papers 2004,32, Humboldt University of Berlin, Center for Applied Statistics and Economics (CASE).
    18. Marcelo Brutti Righi, 2018. "A theory for combinations of risk measures," Papers 1807.01977, arXiv.org, revised May 2023.
    19. Víctor Adame-García & Fernando Fernández-Rodríguez & Simón Sosvilla-Rivero, 2017. "“Resolution of optimization problems and construction of efficient portfolios: An application to the Euro Stoxx 50 index"," IREA Working Papers 201702, University of Barcelona, Research Institute of Applied Economics, revised Feb 2017.
    20. Fang, Yong & Chen, Lihua & Fukushima, Masao, 2008. "A mixed R&D projects and securities portfolio selection model," European Journal of Operational Research, Elsevier, vol. 185(2), pages 700-715, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijfss:v:10:y:2022:i:1:p:20-:d:767153. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.