IDEAS home Printed from https://ideas.repec.org/p/may/mayecw/n1760607.html
   My bibliography  Save this paper

Monte-Carlo Estimations of the Downside Risk of Derivative Portfolios

Author

Listed:
  • Patrick Leoni

    (Economics Department, National University of Ireland, Maynooth)

Abstract

We simulate the performances of a standard derivatives portfolio to evaluate the relevance of benchmarking in terms of downside risk reduction. The simulation shows that benchmarking always leads to significantly more severe losses in average than those generated by letting the portfolio reach the end of a given horizon. Moreover, switching from a 0-correlation across underlyings to a very mild form of correlation significantly increases the probability of reaching the downside benchmark before maturity, whereas adding more correlation does not significantly increase this figure.

Suggested Citation

  • Patrick Leoni, 2007. "Monte-Carlo Estimations of the Downside Risk of Derivative Portfolios," Economics Department Working Paper Series n1760607, Department of Economics, National University of Ireland - Maynooth.
    • Handle: RePEc:may:mayecw:n1760607
    as

    Download full text from publisher

    File URL: http://repec.maynoothuniversity.ie/mayecw-files/N1760607.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jun Liu & Francis A. Longstaff & Jun Pan, 2003. "Dynamic Asset Allocation with Event Risk," Journal of Finance, American Finance Association, vol. 58(1), pages 231-259, February.
    2. Corwin Joy & Phelim P. Boyle & Ken Seng Tan, 1996. "Quasi-Monte Carlo Methods in Numerical Finance," Management Science, INFORMS, vol. 42(6), pages 926-938, June.
    3. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    4. Boyle, Phelim & Broadie, Mark & Glasserman, Paul, 1997. "Monte Carlo methods for security pricing," Journal of Economic Dynamics and Control, Elsevier, vol. 21(8-9), pages 1267-1321, June.
    5. Suleyman Basak & Alex Shapiro & Lucie Teplá, 2006. "Risk Management with Benchmarking," Management Science, INFORMS, vol. 52(4), pages 542-557, April.
    6. Robert Jarrow & Feng Zhao, 2006. "Downside Loss Aversion and Portfolio Management," Management Science, INFORMS, vol. 52(4), pages 558-566, April.
    7. Jérôme Detemple & René Garcia & Marcel Rindisbacher, 2005. "Asymptotic Properties of Monte Carlo Estimators of Derivatives," Management Science, INFORMS, vol. 51(11), pages 1657-1675, November.
    8. Ning Du & David V. Budescu, 2005. "The Effects of Imprecise Probabilities and Outcomes in Evaluating Investment Options," Management Science, INFORMS, vol. 51(12), pages 1791-1803, December.
    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. Leoni, Patrick L., 2009. "Downside risk of derivative portfolios with mean-reverting underlyings," Discussion Papers on Economics 2/2009, University of Southern Denmark, Department of Economics.

    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. Guan, Guohui & Liang, Zongxia, 2016. "Optimal management of DC pension plan under loss aversion and Value-at-Risk constraints," Insurance: Mathematics and Economics, Elsevier, vol. 69(C), pages 224-237.
    2. David Heath & Eckhard Platen, 2002. "A variance reduction technique based on integral representations," Quantitative Finance, Taylor & Francis Journals, vol. 2(5), pages 362-369.
    3. Sun, Yufei & Aw, Grace & Teo, Kok Lay & Zhu, Yanjian & Wang, Xiangyu, 2016. "Multi-period portfolio optimization under probabilistic risk measure," Finance Research Letters, Elsevier, vol. 18(C), pages 60-66.
    4. Aintablian, Sebouh & Khoury, Wissam El, 2017. "A simulation on the presence of competing bidders in mergers and acquisitions," Finance Research Letters, Elsevier, vol. 22(C), pages 233-243.
    5. Okten, Giray & Eastman, Warren, 2004. "Randomized quasi-Monte Carlo methods in pricing securities," Journal of Economic Dynamics and Control, Elsevier, vol. 28(12), pages 2399-2426, December.
    6. Suleyman Basak & Anna Pavlova & Alexander Shapiro, 2007. "Optimal Asset Allocation and Risk Shifting in Money Management," The Review of Financial Studies, Society for Financial Studies, vol. 20(5), pages 1583-1621, 2007 21.
    7. Xiaoqun Wang & Ken Seng Tan, 2013. "Pricing and Hedging with Discontinuous Functions: Quasi-Monte Carlo Methods and Dimension Reduction," Management Science, INFORMS, vol. 59(2), pages 376-389, July.
    8. Divya Aggarwal & Pitabas Mohanty, 2022. "Influence of imprecise information on risk and ambiguity preferences: Experimental evidence," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(4), pages 1025-1038, June.
    9. Fang Liu, 2021. "Regret theory under fear of the unknown," Papers 2108.01825, arXiv.org.
    10. Raimova, Gulnora, 2011. "Variance reduction methods at the pricing of weather options," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 21(1), pages 3-15.
    11. Tak Kuen Siu & Robert J. Elliott, 2019. "Hedging Options In A Doubly Markov-Modulated Financial Market Via Stochastic Flows," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(08), pages 1-41, December.
    12. 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.
    13. Tan, Ken Seng & Boyle, Phelim P., 2000. "Applications of randomized low discrepancy sequences to the valuation of complex securities," Journal of Economic Dynamics and Control, Elsevier, vol. 24(11-12), pages 1747-1782, October.
    14. Andrea Consiglio & Flavio Cocco & Stavros Zenios, 2007. "Scenario optimization asset and liability modelling for individual investors," Annals of Operations Research, Springer, vol. 152(1), pages 167-191, July.
    15. Laure Cabantous & Denis Hilton, 2006. "De l'aversion à l'ambiguïté aux attitudes face à l'ambiguïté. Les apports d'une perspective psychologique en économie," Revue économique, Presses de Sciences-Po, vol. 57(2), pages 259-280.
    16. Velu, C. & Iyer, S., 2008. "The Rationality of Irrationality for Managers: Returns- Based Beliefs and the Traveller’s Dilemma," Cambridge Working Papers in Economics 0826, Faculty of Economics, University of Cambridge.
    17. Nelson Areal & Artur Rodrigues & Manuel Armada, 2008. "On improving the least squares Monte Carlo option valuation method," Review of Derivatives Research, Springer, vol. 11(1), pages 119-151, March.
    18. Xiaoqun Wang & Ian H. Sloan, 2011. "Quasi-Monte Carlo Methods in Financial Engineering: An Equivalence Principle and Dimension Reduction," Operations Research, INFORMS, vol. 59(1), pages 80-95, February.
    19. Flores, Gabriela & O’Donnell, Owen, 2016. "Catastrophic medical expenditure risk," Journal of Health Economics, Elsevier, vol. 46(C), pages 1-15.
    20. Sujoy Chakravarty & Jaideep Roy, 2009. "Recursive expected utility and the separation of attitudes towards risk and ambiguity: an experimental study," Theory and Decision, Springer, vol. 66(3), pages 199-228, March.

    More about this item

    Keywords

    : Derivatives; Portfolio management; Benchmarking; Downside risk; Monte-Carlo simulations.;
    All these keywords.

    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:may:mayecw:n1760607. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/demayie.html .

    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.