IDEAS home Printed from https://ideas.repec.org/a/inm/oropre/v64y2016i2p297-314.html
   My bibliography  Save this article

Handling Discontinuities in Financial Engineering: Good Path Simulation and Smoothing

Author

Listed:
  • Xiaoqun Wang

    (Department of Mathematical Sciences, Tsinghua University, Beijing 100084, China)

Abstract

Discontinuities are common in the pricing and hedging of complex financial derivatives. Quasi-Monte Carlo (QMC) methods for high-dimensional finance problems with discontinuities can be inefficient because of the lack of good smoothness and high dimensionality. Interestingly, path simulation method (PSM) may affect both factors, implying its significance in QMC methods. What defines a “good” PSM for problems with discontinuities? The ability to align the discontinuities with the coordinate axes is a desirable property for a PSM. We show that for an arbitrary PSM, there exists a class of options with discontinuous payoff functions such that the transformed functions have only axis-parallel discontinuities, for which good QMC performance can be expected. In this sense, any PSM can be “good” in QMC methods for a specific class of problems. We analyze the structure of discontinuities for digital options using the new approach and show the superiority and the uniqueness (up to a permutation) of the standard construction. We develop a two-step procedure for pricing and hedging derivatives with discontinuous payoff functions. The first step is to design a good PSM that has the ability to align the discontinuities with the coordinate axes and the second step is to further exploit this nice property to remove the discontinuities completely. We prove that the new estimate is unbiased and has smaller variance. Numerical experiments demonstrate that the two-step procedure is very effective in QMC methods for pricing options and estimating Greeks, leading to a dramatic variance reduction. Both the path simulation step and the smoothing step are crucial and beneficial for QMC methods, with the contribution from each step varying depending on the severity of discontinuity.

Suggested Citation

  • Xiaoqun Wang, 2016. "Handling Discontinuities in Financial Engineering: Good Path Simulation and Smoothing," Operations Research, INFORMS, vol. 64(2), pages 297-314, April.
    • Handle: RePEc:inm:oropre:v:64:y:2016:i:2:p:297-314
    DOI: 10.1287/opre.2015.1470
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/opre.2015.1470
    Download Restriction: no
    File URL: https://libkey.io/10.1287/opre.2015.1470?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
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Spassimir H. Paskov & Joseph F. Traub, 1995. "Faster Valuation of Financial Derivatives," Working Papers 95-03-034, Santa Fe Institute.
    4. Mark Broadie & Paul Glasserman, 1996. "Estimating Security Price Derivatives Using Simulation," Management Science, INFORMS, vol. 42(2), pages 269-285, February.
    5. Liu, Ruixue & Owen, Art B., 2006. "Estimating Mean Dimensionality of Analysis of Variance Decompositions," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 712-721, June.
    6. Xiaoqun Wang, 2006. "On the Effects of Dimension Reduction Techniques on Some High-Dimensional Problems in Finance," Operations Research, INFORMS, vol. 54(6), pages 1063-1078, 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. Ye Xiao & Xiaoqun Wang, 2019. "Enhancing Quasi-Monte Carlo Simulation by Minimizing Effective Dimension for Derivative Pricing," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 343-366, June.
    2. Borgonovo, Emanuele & Rabitti, Giovanni, 2023. "Screening: From tornado diagrams to effective dimensions," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1200-1211.
    3. Lihui Xiong & Ximiao Dong & Jiaqi Fang, 2023. "Interdisciplinary Teaching Reform of Financial Engineering Majors Based on the Analytic Hierarchy Process in the Post-Pandemic Era," Sustainability, MDPI, vol. 15(11), pages 1-17, May.
    4. Harase Shin, 2019. "Comparison of Sobol’ sequences in financial applications," Monte Carlo Methods and Applications, De Gruyter, vol. 25(1), pages 61-74, March.
    5. Zhijian He & Xiaoqun Wang, 2021. "An Integrated Quasi-Monte Carlo Method for Handling High Dimensional Problems with Discontinuities in Financial Engineering," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 693-718, February.

    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. 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.
    2. Nabil Kahalé, 2020. "Randomized Dimension Reduction for Monte Carlo Simulations," Management Science, INFORMS, vol. 66(3), pages 1421-1439, March.
    3. Borgonovo, Emanuele & Rabitti, Giovanni, 2023. "Screening: From tornado diagrams to effective dimensions," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1200-1211.
    4. Borgonovo, Emanuele & Plischke, Elmar, 2016. "Sensitivity analysis: A review of recent advances," European Journal of Operational Research, Elsevier, vol. 248(3), pages 869-887.
    5. Xiaoqun Wang, 2009. "Dimension Reduction Techniques in Quasi-Monte Carlo Methods for Option Pricing," INFORMS Journal on Computing, INFORMS, vol. 21(3), pages 488-504, August.
    6. Ye Xiao & Xiaoqun Wang, 2019. "Enhancing Quasi-Monte Carlo Simulation by Minimizing Effective Dimension for Derivative Pricing," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 343-366, June.
    7. H. Heitsch & H. Leövey & W. Römisch, 2016. "Are Quasi-Monte Carlo algorithms efficient for two-stage stochastic programs?," Computational Optimization and Applications, Springer, vol. 65(3), pages 567-603, December.
    8. Pierre L’Ecuyer, 2009. "Quasi-Monte Carlo methods with applications in finance," Finance and Stochastics, Springer, vol. 13(3), pages 307-349, September.
    9. 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.
    10. 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.
    11. Marco Bianchetti & Sergei Kucherenko & Stefano Scoleri, 2015. "Pricing and Risk Management with High-Dimensional Quasi Monte Carlo and Global Sensitivity Analysis," Papers 1504.02896, arXiv.org.
    12. Sobol Ilya M. & Shukhman Boris V., 2014. "Quasi-Monte Carlo: A high-dimensional experiment," Monte Carlo Methods and Applications, De Gruyter, vol. 20(3), pages 167-171, September.
    13. Yang, Jun & He, Ping & Fang, Kai-Tai, 2022. "Three kinds of discrete approximations of statistical multivariate distributions and their applications," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    14. Chao Yu & Xiaoqun Wang, 2023. "Quasi-Monte Carlo-Based Conditional Malliavin Method for Continuous-Time Asian Option Greeks," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 325-360, June.
    15. He, Zhijian, 2022. "Sensitivity estimation of conditional value at risk using randomized quasi-Monte Carlo," European Journal of Operational Research, Elsevier, vol. 298(1), pages 229-242.
    16. Jan Baldeaux & Dale Roberts, 2012. "Quasi-Monte Carol Methods for the Heston Model," Research Paper Series 307, Quantitative Finance Research Centre, University of Technology, Sydney.
    17. Ballotta, Laura & Eberlein, Ernst & Schmidt, Thorsten & Zeineddine, Raghid, 2021. "Fourier based methods for the management of complex life insurance products," Insurance: Mathematics and Economics, Elsevier, vol. 101(PB), pages 320-341.
    18. Mark Broadie & Jerome B. Detemple, 2004. "ANNIVERSARY ARTICLE: Option Pricing: Valuation Models and Applications," Management Science, INFORMS, vol. 50(9), pages 1145-1177, September.
    19. F. Y. Kuo & W. T. M. Dunsmuir & I. H. Sloan & M. P. Wand & R. S. Womersley, 2008. "Quasi-Monte Carlo for Highly Structured Generalised Response Models," Methodology and Computing in Applied Probability, Springer, vol. 10(2), pages 239-275, June.
    20. Zhijian He & Xiaoqun Wang, 2021. "An Integrated Quasi-Monte Carlo Method for Handling High Dimensional Problems with Discontinuities in Financial Engineering," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 693-718, February.

    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:inm:oropre:v:64:y:2016:i:2:p:297-314. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.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.