IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1610.09622.html
   My bibliography  Save this paper

Numerical study of splitting methods for American option valuation

Author

Listed:
  • Karel in 't Hout
  • Radoslav Valkov

Abstract

This paper deals with the numerical approximation of American-style option values governed by partial differential complementarity problems. For a variety of one- and two-asset American options we investigate by ample numerical experiments the temporal convergence behaviour of three modern splitting methods: the explicit payoff approach, the Ikonen-Toivanen approach and the Peaceman-Rachford method. In addition, the temporal accuracy of these splitting methods is compared to that of the penalty approach.

Suggested Citation

  • Karel in 't Hout & Radoslav Valkov, 2016. "Numerical study of splitting methods for American option valuation," Papers 1610.09622, arXiv.org.
  • Handle: RePEc:arx:papers:1610.09622
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1610.09622
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tinne Haentjens & Karel J. in 't Hout, 2015. "ADI Schemes for Pricing American Options under the Heston Model," Applied Mathematical Finance, Taylor & Francis Journals, vol. 22(3), pages 207-237, July.
    2. Sam Howison & Christoph Reisinger & Jan Hendrik Witte, 2010. "The Effect of Non-Smooth Payoffs on the Penalty Approximation of American Options," Papers 1008.0836, arXiv.org, revised May 2013.
    Full references (including those not matched with items on IDEAS)

    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. Kathrin Glau & Daniel Kressner & Francesco Statti, 2019. "Low-rank tensor approximation for Chebyshev interpolation in parametric option pricing," Papers 1902.04367, arXiv.org.
    2. Louis Bhim & Reiichiro Kawai, 2018. "Smooth Upper Bounds For The Price Function Of American Style Options," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 21(01), pages 1-38, February.
    3. Andersson, Kristoffer & Oosterlee, Cornelis W., 2021. "A deep learning approach for computations of exposure profiles for high-dimensional Bermudan options," Applied Mathematics and Computation, Elsevier, vol. 408(C).
    4. Yiannis A. Papadopoulos & Alan L. Lewis, 2018. "A First Option Calibration of the GARCH Diffusion Model by a PDE Method," Papers 1801.06141, arXiv.org.
    5. Tber, Moulay Hicham, 2023. "A semi-Lagrangian mixed finite element method for advection–diffusion variational inequalities," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 204(C), pages 202-215.
    6. Karel J. in’t Hout & Jacob Snoeijer, 2021. "Numerical Valuation of American Basket Options via Partial Differential Complementarity Problems," Mathematics, MDPI, vol. 9(13), pages 1-17, June.
    7. Maryam Safaei & Abodolsadeh Neisy & Nader Nematollahi, 2018. "New Splitting Scheme for Pricing American Options Under the Heston Model," Computational Economics, Springer;Society for Computational Economics, vol. 52(2), pages 405-420, August.
    8. Karel in 't Hout & Jacob Snoeijer, 2021. "Numerical valuation of American basket options via partial differential complementarity problems," Papers 2106.01200, arXiv.org.
    9. Kim, See-Woo & Kim, Jeong-Hoon, 2018. "Analytic solutions for variance swaps with double-mean-reverting volatility," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 130-144.
    10. Andersson, Kristoffer & Oosterlee, Cornelis W., 2021. "Deep learning for CVA computations of large portfolios of financial derivatives," Applied Mathematics and Computation, Elsevier, vol. 409(C).
    11. Cornelis S. L. de Graaf & Drona Kandhai & Christoph Reisinger, 2016. "Efficient exposure computation by risk factor decomposition," Papers 1608.01197, arXiv.org, revised Feb 2018.
    12. Yangyang Zhuang & Pan Tang, 2023. "Pricing of American Parisian option as executive option based on the least‐squares Monte Carlo approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(10), pages 1469-1496, October.
    13. Kathrin Glau & Mirco Mahlstedt & Christian Potz, 2018. "A new approach for American option pricing: The Dynamic Chebyshev method," Papers 1806.05579, arXiv.org.
    14. Slobodan Milovanovi'c, 2018. "Pricing Financial Derivatives using Radial Basis Function generated Finite Differences with Polyharmonic Splines on Smoothly Varying Node Layouts," Papers 1808.02365, arXiv.org, revised Aug 2018.
    15. Blessing Taruvinga & Boda Kang & Christina Sklibosios Nikitopoulos, 2018. "Pricing American Options with Jumps in Asset and Volatility," Research Paper Series 394, Quantitative Finance Research Centre, University of Technology, Sydney.
    16. Reza Mollapourasl & Ali Fereshtian & Michèle Vanmaele, 2019. "Radial Basis Functions with Partition of Unity Method for American Options with Stochastic Volatility," Computational Economics, Springer;Society for Computational Economics, vol. 53(1), pages 259-287, January.
    17. M. Khasi & J. Rashidinia, 2024. "A Bilinear Pseudo-spectral Method for Solving Two-asset European and American Pricing Options," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 893-918, February.
    18. Belssing Taruvinga, 2019. "Solving Selected Problems on American Option Pricing with the Method of Lines," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 4-2019, January-A.
    19. Maciej Balajewicz & Jari Toivanen, 2016. "Reduced Order Models for Pricing European and American Options under Stochastic Volatility and Jump-Diffusion Models," Papers 1612.00402, arXiv.org.
    20. Purba Banerjee & Vasudeva Murthy & Shashi Jain, 2024. "Method of Lines for Valuation and Sensitivities of Bermudan Options," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 245-270, January.

    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:1610.09622. 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.