IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v166y2009i1p5-2210.1007-s10479-008-0441-z.html
   My bibliography  Save this article

Use of stochastic and mathematical programming in portfolio theory and practice

Author

Listed:
  • William Ziemba

Abstract

Standard finance portfolio theory draws graphs and writes equations usually with no constraints and frequently in the univariate case. However, in reality, there are multivariate random variables and multivariate asset weights to determine with constraints. Also there are the effects of transaction costs on asset prices in the theory and calculation of optimal portfolios in the static and dynamic cases. There we use various stochastic programming, linear complementary, quadratic programming and nonlinear programming problems. This paper begins with the simplest problems and builds the theory to the more complex cases and then applies it to real financial asset allocation problems, hedge funds and professional racetrack betting. Copyright Springer Science+Business Media, LLC 2009

Suggested Citation

  • William Ziemba, 2009. "Use of stochastic and mathematical programming in portfolio theory and practice," Annals of Operations Research, Springer, vol. 166(1), pages 5-22, February.
    • Handle: RePEc:spr:annopr:v:166:y:2009:i:1:p:5-22:10.1007/s10479-008-0441-z
    DOI: 10.1007/s10479-008-0441-z
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10479-008-0441-z
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10479-008-0441-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Donald B. Hausch & Victor S. Y. Lo & William T. Ziemba, 2008. "Introduction to the Efficiency of Racetrack Betting Markets in England," World Scientific Book Chapters, in: Donald B Hausch & Victor SY Lo & William T Ziemba (ed.), Efficiency Of Racetrack Betting Markets, chapter 51, pages 529-531, World Scientific Publishing Co. Pte. Ltd..
    2. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    3. J. G. Kallberg & W. T. Ziemba, 1983. "Comparison of Alternative Utility Functions in Portfolio Selection Problems," Management Science, INFORMS, vol. 29(11), pages 1257-1276, November.
    4. G. Hanoch & H. Levy, 1969. "The Efficiency Analysis of Choices Involving Risk," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 36(3), pages 335-346.
    5. J. Tobin, 1958. "Liquidity Preference as Behavior Towards Risk," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 25(2), pages 65-86.
    6. Alois Geyer & William T. Ziemba, 2008. "The Innovest Austrian Pension Fund Financial Planning Model InnoALM," Operations Research, INFORMS, vol. 56(4), pages 797-810, August.
    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. I-Chen Lu & Kai-Hong Tee & Baibing Li, 2019. "Asset allocation with multiple analysts’ views: a robust approach," Journal of Asset Management, Palgrave Macmillan, vol. 20(3), pages 215-228, May.
    2. Bae, Geum Il & Kim, Woo Chang & Mulvey, John M., 2014. "Dynamic asset allocation for varied financial markets under regime switching framework," European Journal of Operational Research, Elsevier, vol. 234(2), pages 450-458.

    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. Wong, Wing-Keung & Phoon, Kok Fai & Lean, Hooi Hooi, 2008. "Stochastic dominance analysis of Asian hedge funds," Pacific-Basin Finance Journal, Elsevier, vol. 16(3), pages 204-223, June.
    2. Lean, Hooi Hooi & McAleer, Michael & Wong, Wing-Keung, 2010. "Market efficiency of oil spot and futures: A mean-variance and stochastic dominance approach," Energy Economics, Elsevier, vol. 32(5), pages 979-986, September.
    3. Ballestero, E. & Gunther, M. & Pla-Santamaria, D. & Stummer, C., 2007. "Portfolio selection under strict uncertainty: A multi-criteria methodology and its application to the Frankfurt and Vienna Stock Exchanges," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1476-1487, September.
    4. Hooi Hooi Lean & Michael McAleer & Wing-Keung Wong, 2010. "Market Efficiency of Oil Spot and Futures: A Stochastic Dominance Approach," CIRJE F-Series CIRJE-F-705, CIRJE, Faculty of Economics, University of Tokyo.
    5. Lean, H.H. & McAleer, M.J. & Wong, W.-K., 2010. "Investor preferences for oil spot and futures based on mean-variance and stochastic dominance," Econometric Institute Research Papers EI 2010-37, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    6. Merton, Robert, 1990. "Capital market theory and the pricing of financial securities," Handbook of Monetary Economics, in: B. M. Friedman & F. H. Hahn (ed.), Handbook of Monetary Economics, edition 1, volume 1, chapter 11, pages 497-581, Elsevier.
    7. Levy, Haim & Levy, Moshe, 2021. "Stocks versus bonds for the long run when a riskless asset is available," Journal of Banking & Finance, Elsevier, vol. 133(C).
    8. Kai-Yin Woo & Chulin Mai & Michael McAleer & Wing-Keung Wong, 2020. "Review on Efficiency and Anomalies in Stock Markets," Economies, MDPI, vol. 8(1), pages 1-51, March.
    9. Bai, Zhidong & Phoon, Kok Fai & Wang, Keyan & Wong, Wing-Keung, 2013. "The performance of commodity trading advisors: A mean-variance-ratio test approach," The North American Journal of Economics and Finance, Elsevier, vol. 25(C), pages 188-201.
    10. Levy, Haim & Levy, Moshe, 2014. "The benefits of differential variance-based constraints in portfolio optimization," European Journal of Operational Research, Elsevier, vol. 234(2), pages 372-381.
    11. Haim Levy, 2017. "What is the Economic Cost of the Investment Home Bias?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(5), pages 897-929, August.
    12. Gautam Mitra & Frank Ellison & Alan Scowcroft, 2007. "Quadratic programming for portfolio planning: Insights into algorithmic and computational issues," Journal of Asset Management, Palgrave Macmillan, vol. 8(3), pages 200-214, September.
    13. Joro, Tarja & Na, Paul, 2006. "Portfolio performance evaluation in a mean-variance-skewness framework," European Journal of Operational Research, Elsevier, vol. 175(1), pages 446-461, November.
    14. Haim Levy & Zvi Lerman, 1988. "Testing The Predictive Power Of Ex-Post Efficient Portfolios," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 11(3), pages 241-254, September.
    15. Dipankar Mondal & N. Selvaraju, 2022. "Convexity, two-fund separation and asset ranking in a mean-LPM portfolio selection framework," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(1), pages 225-248, March.
    16. Anders Johansson, 2009. "An analysis of dynamic risk in the Greater China equity markets," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 7(3), pages 299-320.
    17. Chia-Lin Chang & Jukka Ilomäki & Hannu Laurila & Michael McAleer, 2018. "Long Run Returns Predictability and Volatility with Moving Averages," Risks, MDPI, vol. 6(4), pages 1-18, September.
    18. Hany Shawky & Ronald Forbes & Alan Frankle, 1983. "Liquidity Services and Capital Market Equilibrium: The Case for Money Market Mutual Funds," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 6(2), pages 141-152, June.
    19. Hooi Hooi Lean & Michael McAleer & Wing-Keung Wong, 2013. "Risk-averse and Risk-seeking Investor Preferences for Oil Spot and Futures," Documentos de Trabajo del ICAE 2013-31, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico, revised Aug 2013.
    20. Raymond H. Chan & Ephraim Clark & Xu Guo & Wing-Keung Wong, 2020. "New development on the third-order stochastic dominance for risk-averse and risk-seeking investors with application in risk management," Risk Management, Palgrave Macmillan, vol. 22(2), pages 108-132, June.

    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:spr:annopr:v:166:y:2009:i:1:p:5-22:10.1007/s10479-008-0441-z. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.