IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v100y2000i1p227-24910.1023-a1019296422231.html
   My bibliography  Save this article

A Stochastic Programming Model for Currency Option Hedging

Author

Listed:
  • Jason Wu
  • Suvrajeet Sen

Abstract

In this paper we use a stochastic programming approach to develop currency option hedging models which can address problems with multiple random factors in an imperfect market. The portfolios considered in our model are rebalanced at the end of each time period, and reinvestments are allowed during the hedging process. These sequential decisions (reinvestments) are based on the evolution of random parameters such as exchange rates, interest rates, etc. We also allow the inclusion of a variety of instruments in the hedging portfolio, including short term derivative securities, short term options, and futures. These instruments help generate strategies that provide good liquidity and low trade intensity. One of the important features of the model is that it incorporates constraints on sensitivity measures such as Delta and Gamma. By ensuring that these hedge parameters track a desired trajectory (e.g., the parameters of a target option), the new model provides investment strategies that are robust with respect to the perturbations measured by Delta and Gamma. In order to manage the explosion of scenarios due to multiple random factors, we incorporate sampling within a scenario aggregation algorithm. We illustrate that when compared with other myopic hedging methods in imperfect markets, the new stochastic programming model can provide better performance. Our examples also illustrate stochastic programming as a practical computational tool for realistic hedging problems. Copyright Kluwer Academic Publishers 2000

Suggested Citation

  • Jason Wu & Suvrajeet Sen, 2000. "A Stochastic Programming Model for Currency Option Hedging," Annals of Operations Research, Springer, vol. 100(1), pages 227-249, December.
    • Handle: RePEc:spr:annopr:v:100:y:2000:i:1:p:227-249:10.1023/a:1019296422231
    DOI: 10.1023/A:1019296422231
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1023/A:1019296422231
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1023/A:1019296422231?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.

    Citations

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


    Cited by:

    1. Libo Yin & Liyan Han, 2013. "Options strategies for international portfolios with overall risk management via multi-stage stochastic programming," Annals of Operations Research, Springer, vol. 206(1), pages 557-576, July.
    2. Blomvall, Jörgen & Hagenbjörk, Johan, 2022. "Reducing transaction costs for interest rate risk hedging with stochastic programming," European Journal of Operational Research, Elsevier, vol. 302(3), pages 1282-1293.
    3. Valeriy Ryabchenko & Sergey Sarykalin & Stan Uryasev, 2004. "Pricing European Options by Numerical Replication: Quadratic Programming with Constraints," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 11(3), pages 301-333, September.
    4. Libo Yin & Liyan Han, 2015. "Hedging International Foreign Exchange Risks via Option Based Portfolio Insurance," Computational Economics, Springer;Society for Computational Economics, vol. 45(1), pages 151-181, January.
    5. Suvrajeet Sen & Lihua Yu & Talat Genc, 2006. "A Stochastic Programming Approach to Power Portfolio Optimization," Operations Research, INFORMS, vol. 54(1), pages 55-72, February.
    6. Gao, Pei-wang, 2009. "Options strategies with the risk adjustment," European Journal of Operational Research, Elsevier, vol. 192(3), pages 975-980, February.
    7. Barro, Diana & Consigli, Giorgio & Varun, Vivek, 2022. "A stochastic programming model for dynamic portfolio management with financial derivatives," Journal of Banking & Finance, Elsevier, vol. 140(C).
    8. Xiangling Hu & Charles Munson & Stergios Fotopoulos, 2012. "Purchasing decisions under stochastic prices: Approximate solutions for order time, order quantity and supplier selection," Annals of Operations Research, Springer, vol. 201(1), pages 287-305, December.
    9. Topaloglou, Nikolas & Vladimirou, Hercules & Zenios, Stavros A., 2020. "Integrated dynamic models for hedging international portfolio risks," European Journal of Operational Research, Elsevier, vol. 285(1), pages 48-65.

    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:100:y:2000:i:1:p:227-249:10.1023/a:1019296422231. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.