IDEAS home Printed from https://ideas.repec.org/a/spr/decfin/v42y2019i2d10.1007_s10203-019-00249-8.html
   My bibliography  Save this article

Robust calibration and arbitrage-free interpolation of SSVI slices

Author

Listed:
  • Jacopo Corbetta

    (Zeliade Systems)

  • Pierre Cohort

    (Zeliade Systems)

  • Ismail Laachir

    (Zeliade Systems)

  • Claude Martini

    (Zeliade Systems)

Abstract

We describe a robust calibration algorithm of a set of SSVI maturity slices (i.e., a set of 3 SSVI parameters $$\theta _t, \rho _t, \varphi _t$$θt,ρt,φt attached to each option maturity t available on the market), which grants that these slices are free of butterfly and of calendar spread arbitrage. Given such a set of consistent SSVI parameters, we show that the most natural interpolation/extrapolation of the parameters provides a full continuous volatility surface free of arbitrage. The numerical implementation is straightforward, robust and quick, yielding an effective and parsimonious solution to the smile problem, which has the potential to become a benchmark one. We thank Antoine Jacquier and Stefano De Marco for useful discussions and remarks. All remaining errors are ours.

Suggested Citation

  • Jacopo Corbetta & Pierre Cohort & Ismail Laachir & Claude Martini, 2019. "Robust calibration and arbitrage-free interpolation of SSVI slices," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 42(2), pages 665-677, December.
  • Handle: RePEc:spr:decfin:v:42:y:2019:i:2:d:10.1007_s10203-019-00249-8
    DOI: 10.1007/s10203-019-00249-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10203-019-00249-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10203-019-00249-8?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. Jim Gatheral & Antoine Jacquier, 2014. "Arbitrage-free SVI volatility surfaces," Quantitative Finance, Taylor & Francis Journals, vol. 14(1), pages 59-71, January.
    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. Elisa Alòs & Maria Elvira Mancino & Tai-Ho Wang, 2019. "Volatility and volatility-linked derivatives: estimation, modeling, and pricing," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 42(2), pages 321-349, December.
    2. Mehdi El Amrani & Antoine Jacquier & Claude Martini, 2019. "Dynamics of symmetric SSVI smiles and implied volatility bubbles," Papers 1909.10272, arXiv.org, revised Feb 2021.
    3. Claude Martini & Arianna Mingone, 2020. "No arbitrage SVI," Papers 2005.03340, arXiv.org, revised May 2021.

    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. Stefano De Marco, 2020. "On the harmonic mean representation of the implied volatility," Papers 2007.03585, arXiv.org.
    2. H. Peter Boswijk & Roger J. A. Laeven & Evgenii Vladimirov, 2022. "Estimating Option Pricing Models Using a Characteristic Function-Based Linear State Space Representation," Papers 2210.06217, arXiv.org.
    3. Ciprian Necula & Gabriel Drimus & Walter Farkas, 2019. "A general closed form option pricing formula," Review of Derivatives Research, Springer, vol. 22(1), pages 1-40, April.
    4. Vedant Choudhary & Sebastian Jaimungal & Maxime Bergeron, 2023. "FuNVol: A Multi-Asset Implied Volatility Market Simulator using Functional Principal Components and Neural SDEs," Papers 2303.00859, arXiv.org, revised Dec 2023.
    5. Sergey Badikov & Mark H. A. Davis & Antoine Jacquier, 2018. "Perturbation analysis of sub/super hedging problems," Papers 1806.03543, arXiv.org, revised May 2021.
    6. Bastien Baldacci, 2020. "High-frequency dynamics of the implied volatility surface," Papers 2012.10875, arXiv.org.
    7. Benjamin Virrion, 2020. "Deep Importance Sampling," Papers 2007.02692, arXiv.org, revised Jul 2020.
    8. Daniel Guterding, 2023. "Sparse Modeling Approach to the Arbitrage-Free Interpolation of Plain-Vanilla Option Prices and Implied Volatilities," Risks, MDPI, vol. 11(5), pages 1-24, April.
    9. Thaddeus Neururer & George Papadakis & Edward J. Riedl, 2016. "Tests of investor learning models using earnings innovations and implied volatilities," Review of Accounting Studies, Springer, vol. 21(2), pages 400-437, June.
    10. Matic, Jovanka Lili & Packham, Natalie & Härdle, Wolfgang Karl, 2021. "Hedging Cryptocurrency Options," MPRA Paper 110985, University Library of Munich, Germany.
    11. Niu, Jing & Ma, Chao & Wang, Yunpeng & Chang, Chun-Ping & Wang, Haijie, 2022. "The pricing of China stock index options based on monetary policy uncertainty," Journal of Asian Economics, Elsevier, vol. 81(C).
    12. Dillschneider, Yannick & Maurer, Raimond, 2019. "Functional Ross recovery: Theoretical results and empirical tests," Journal of Economic Dynamics and Control, Elsevier, vol. 108(C).
    13. Mnacho Echenim & Emmanuel Gobet & Anne-Claire Maurice, 2022. "Unbiasing and robustifying implied volatility calibration in a cryptocurrency market with large bid-ask spreads and missing quotes," Papers 2207.02989, arXiv.org.
    14. Wenyong Zhang & Lingfei Li & Gongqiu Zhang, 2021. "A Two-Step Framework for Arbitrage-Free Prediction of the Implied Volatility Surface," Papers 2106.07177, arXiv.org, revised Jan 2022.
    15. Arianna Mingone, 2022. "Smiles in delta," Papers 2209.00406, arXiv.org.
    16. Stefano De Marco & Caroline Hillairet & Antoine Jacquier, 2013. "Shapes of implied volatility with positive mass at zero," Papers 1310.1020, arXiv.org, revised May 2017.
    17. Mnacho Echenim & Emmanuel Gobet & Anne-Claire Maurice, 2022. "Unbiasing and robustifying implied volatility calibration in a cryptocurrency market with large bid-ask spreads and missing quotes," Working Papers hal-03715921, HAL.
    18. Brian Huge & Antoine Savine, 2020. "Differential Machine Learning," Papers 2005.02347, arXiv.org, revised Sep 2020.
    19. Bender Christian & Thiel Matthias, 2020. "Arbitrage-free interpolation of call option prices," Statistics & Risk Modeling, De Gruyter, vol. 37(1-2), pages 55-78, January.
    20. Chun Yat Yeung & Ali Hirsa, 2022. "Saddle-Point Approach to Large-Time Volatility Smile," Papers 2212.05671, arXiv.org.

    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:decfin:v:42:y:2019:i:2:d:10.1007_s10203-019-00249-8. 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.