IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1210.5859.html
   My bibliography  Save this paper

Determination the Parameters of Markowitz Portfolio Optimization Model

Author

Listed:
  • Ertugrul Bayraktar
  • Ayse Humeyra Bilge

Abstract

The main purpose of this study is the determination of the optimal length of the historical data for the estimation of statistical parameters in Markowitz Portfolio Optimization. We present a trading simulation using Markowitz method, for a portfolio consisting of foreign currency exchange rates and selected assets from the Istanbul Stock Exchange ISE 30, over the period 2001-2009. In the simulation, the expected returns and the covariance matrix are computed from historical data observed for past n days and the target returns are chosen as multiples of the return of the market index. The trading strategy is to buy a stock if the simulation resulted in a feasible solution and sell the stock after exactly m days, independently from the market conditions. The actual returns are computed for n and m being equal to 21, 42, 63, 84 and 105 days and we have seen that the best return is obtained when the observation period is 2 or 3 times the investment period.

Suggested Citation

  • Ertugrul Bayraktar & Ayse Humeyra Bilge, 2012. "Determination the Parameters of Markowitz Portfolio Optimization Model," Papers 1210.5859, arXiv.org.
    • Handle: RePEc:arx:papers:1210.5859
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1210.5859
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. repec:oup:jfinec:v:10:y:2012:i:1:p:164-197 is not listed on IDEAS
    2. Bertille Antoine, 2010. "Portfolio Selection with Estimation Risk: A Test-Based Approach," Journal of Financial Econometrics, Oxford University Press, vol. 10(1), pages 164-197, 2012 10 1.
    3. Hiroshi Konno & Hiroaki Yamazaki, 1991. "Mean-Absolute Deviation Portfolio Optimization Model and Its Applications to Tokyo Stock Market," Management Science, INFORMS, vol. 37(5), pages 519-531, May.
    4. Leung, Pui-Lam & Ng, Hon-Yip & Wong, Wing-Keung, 2012. "An improved estimation to make Markowitz’s portfolio optimization theory users friendly and estimation accurate with application on the US stock market investment," European Journal of Operational Research, Elsevier, vol. 222(1), pages 85-95.
    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. Gutierrez, Tomás & Pagnoncelli, Bernardo & Valladão, Davi & Cifuentes, Arturo, 2019. "Can asset allocation limits determine portfolio risk–return profiles in DC pension schemes?," Insurance: Mathematics and Economics, Elsevier, vol. 86(C), pages 134-144.
    2. Pagnoncelli, Bernardo K. & Cifuentes, Arturo & Denis, Gabriela, 2017. "A two-step hybrid investment strategy for pension funds," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 574-583.

    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. Li, Hua & Bai, Zhidong & Wong, Wing-Keung & McAleer, Michael, 2022. "Spectrally-Corrected Estimation for High-Dimensional Markowitz Mean-Variance Optimization," Econometrics and Statistics, Elsevier, vol. 24(C), pages 133-150.
    2. Bai, Zhidong & Liu, Huixia & Wong, Wing-Keung, 2016. "Making Markowitz's Portfolio Optimization Theory Practically Useful," MPRA Paper 74360, University Library of Munich, Germany.
    3. Bai, Zhidong & Li, Hua & Wong, Wing-Keung, 2013. "The best estimation for high-dimensional Markowitz mean-variance optimization," MPRA Paper 43862, University Library of Munich, Germany.
    4. Gupta, Pankaj & Mittal, Garima & Mehlawat, Mukesh Kumar, 2013. "Expected value multiobjective portfolio rebalancing model with fuzzy parameters," Insurance: Mathematics and Economics, Elsevier, vol. 52(2), pages 190-203.
    5. Giovanni Bonaccolto & Massimiliano Caporin & Sandra Paterlini, 2018. "Asset allocation strategies based on penalized quantile regression," Computational Management Science, Springer, vol. 15(1), pages 1-32, January.
    6. Wojtek Michalowski & Włodzimierz Ogryczak, 2001. "Extending the MAD portfolio optimization model to incorporate downside risk aversion," Naval Research Logistics (NRL), John Wiley & Sons, vol. 48(3), pages 185-200, April.
    7. Ogryczak, Wlodzimierz & Ruszczynski, Andrzej, 1999. "From stochastic dominance to mean-risk models: Semideviations as risk measures," European Journal of Operational Research, Elsevier, vol. 116(1), pages 33-50, July.
    8. Fan, Jianqing & Liao, Yuan & Shi, Xiaofeng, 2015. "Risks of large portfolios," Journal of Econometrics, Elsevier, vol. 186(2), pages 367-387.
    9. Najafi, Alireza & Taleghani, Rahman, 2022. "Fractional Liu uncertain differential equation and its application to finance," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).
    10. Liu, Yong-Jun & Zhang, Wei-Guo, 2015. "A multi-period fuzzy portfolio optimization model with minimum transaction lots," European Journal of Operational Research, Elsevier, vol. 242(3), pages 933-941.
    11. Milan Vaclavik & Josef Jablonsky, 2012. "Revisions of modern portfolio theory optimization model," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(3), pages 473-483, September.
    12. Abdelbari El Khamlichi & Thi Hong Van Hoang & Wing‐keung Wong, 2016. "Is Gold Different for Islamic and Conventional Portfolios? A Sectorial Analysis," Post-Print hal-02965765, HAL.
    13. 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.
    14. Walter Murray & Howard Shek, 2012. "A local relaxation method for the cardinality constrained portfolio optimization problem," Computational Optimization and Applications, Springer, vol. 53(3), pages 681-709, December.
    15. Francesco Cesarone & Andrea Scozzari & Fabio Tardella, 2015. "Linear vs. quadratic portfolio selection models with hard real-world constraints," Computational Management Science, Springer, vol. 12(3), pages 345-370, July.
    16. Hoang, Thi-Hong-Van & Wong, Wing-Keung & Zhu, Zhenzhen, 2015. "Is gold different for risk-averse and risk-seeking investors? An empirical analysis of the Shanghai Gold Exchange," Economic Modelling, Elsevier, vol. 50(C), pages 200-211.
    17. 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.
    18. Chang, C-L. & McAleer, M.J. & Wong, W.-K., 2015. "Informatics, Data Mining, Econometrics and Financial Economics: A Connection," Econometric Institute Research Papers EI2015-34, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    19. Ahmar, Ansari Saleh & Arifin, Andi Nurani Mangkawani, 2017. "Optimalisasi Risiko Saham Menggunakan Optimalisasi Portofolio Markowitz (Studi Kasus Saham Di Indonesia)," INA-Rxiv 5v27k, Center for Open Science.
    20. Chia-Lin Chang & Michael McAleer & Wing-Keung Wong, 2018. "Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology: Connections," JRFM, MDPI, vol. 11(1), pages 1-29, March.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:arx:papers:1210.5859. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.