IDEAS home Printed from https://ideas.repec.org/a/spr/jotpro/v35y2022i4d10.1007_s10959-022-01157-1.html
   My bibliography  Save this article

A Limit Theorem for Bernoulli Convolutions and the $$\Phi $$ Φ -Variation of Functions in the Takagi Class

Author

Listed:
  • Xiyue Han

    (University of Waterloo)

  • Alexander Schied

    (University of Waterloo)

  • Zhenyuan Zhang

    (Stanford University)

Abstract

We consider a probabilistic approach to compute the Wiener–Young $$\Phi $$ Φ -variation of fractal functions in the Takagi class. Here, the $$\Phi $$ Φ -variation is understood as a generalization of the quadratic variation or, more generally, the pth variation of a trajectory computed along the sequence of dyadic partitions of the unit interval. The functions $$\Phi $$ Φ we consider form a very wide class of functions that are regularly varying at zero. Moreover, for each such function $$\Phi $$ Φ , our results provide in a straightforward manner a large and tractable class of functions that have nontrivial and linear $$\Phi $$ Φ -variation. As a corollary, we also construct stochastic processes whose sample paths have nontrivial, deterministic, and linear $$\Phi $$ Φ -variation for each function $$\Phi $$ Φ from our class. The proof of our main result relies on a limit theorem for certain sums of Bernoulli random variables that converge to an infinite Bernoulli convolution.

Suggested Citation

  • Xiyue Han & Alexander Schied & Zhenyuan Zhang, 2022. "A Limit Theorem for Bernoulli Convolutions and the $$\Phi $$ Φ -Variation of Functions in the Takagi Class," Journal of Theoretical Probability, Springer, vol. 35(4), pages 2853-2878, December.
  • Handle: RePEc:spr:jotpro:v:35:y:2022:i:4:d:10.1007_s10959-022-01157-1
    DOI: 10.1007/s10959-022-01157-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10959-022-01157-1
    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/s10959-022-01157-1?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 & Thibault Jaisson & Mathieu Rosenbaum, 2018. "Volatility is rough," Quantitative Finance, Taylor & Francis Journals, vol. 18(6), pages 933-949, June.
    2. Han, Xiyue & Schied, Alexander & Zhang, Zhenyuan, 2021. "A probabilistic approach to the Φ-variation of classical fractal functions with critical roughness," Statistics & Probability Letters, Elsevier, vol. 168(C).
    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. Xiyue Han & Alexander Schied, 2021. "The roughness exponent and its model-free estimation," Papers 2111.10301, arXiv.org, revised Aug 2023.
    2. Josselin Garnier & Knut Sølna, 2018. "Option pricing under fast-varying and rough stochastic volatility," Annals of Finance, Springer, vol. 14(4), pages 489-516, November.
    3. Christian Bayer & Peter K. Friz & Paul Gassiat & Jorg Martin & Benjamin Stemper, 2020. "A regularity structure for rough volatility," Mathematical Finance, Wiley Blackwell, vol. 30(3), pages 782-832, July.
    4. Eduardo Abi Jaber, 2022. "The characteristic function of Gaussian stochastic volatility models: an analytic expression," Working Papers hal-02946146, HAL.
    5. 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.
    6. Carsten H. Chong & Viktor Todorov, 2023. "Asymptotic Expansions for High-Frequency Option Data," Papers 2304.12450, arXiv.org.
    7. Eduardo Abi Jaber, 2022. "The characteristic function of Gaussian stochastic volatility models: an analytic expression," Finance and Stochastics, Springer, vol. 26(4), pages 733-769, October.
    8. Xiyue Han & Alexander Schied, 2022. "Robust Faber--Schauder approximation based on discrete observations of an antiderivative," Papers 2211.11907, arXiv.org, revised Aug 2023.
    9. Calypso Herrera & Florian Krach & Pierre Ruyssen & Josef Teichmann, 2021. "Optimal Stopping via Randomized Neural Networks," Papers 2104.13669, arXiv.org, revised Dec 2023.
    10. Bastien Baldacci, 2020. "High-frequency dynamics of the implied volatility surface," Papers 2012.10875, arXiv.org.
    11. Masaaki Fukasawa & Tetsuya Takabatake & Rebecca Westphal, 2019. "Is Volatility Rough ?," Papers 1905.04852, arXiv.org, revised May 2019.
    12. Matthieu Garcin, 2021. "Forecasting with fractional Brownian motion: a financial perspective," Papers 2105.09140, arXiv.org, revised Sep 2021.
    13. Sigurd Emil Rømer & Rolf Poulsen, 2020. "How Does the Volatility of Volatility Depend on Volatility?," Risks, MDPI, vol. 8(2), pages 1-18, June.
    14. Daniel Bartl & Michael Kupper & David J. Prömel & Ludovic Tangpi, 2019. "Duality for pathwise superhedging in continuous time," Finance and Stochastics, Springer, vol. 23(3), pages 697-728, July.
    15. Hans Buhler & Blanka Horvath & Terry Lyons & Imanol Perez Arribas & Ben Wood, 2020. "A Data-driven Market Simulator for Small Data Environments," Papers 2006.14498, arXiv.org.
    16. Mathieu Rosenbaum & Jianfei Zhang, 2022. "On the universality of the volatility formation process: when machine learning and rough volatility agree," Papers 2206.14114, arXiv.org.
    17. Mehrdoust, Farshid & Noorani, Idin & Hamdi, Abdelouahed, 2023. "Two-factor Heston model equipped with regime-switching: American option pricing and model calibration by Levenberg–Marquardt optimization algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 204(C), pages 660-678.
    18. Wang, Qiyu & Chong, Terence Tai-Leung, 2021. "Factor pricing of cryptocurrencies," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    19. Liang Wang & Weixuan Xia, 2022. "Power‐type derivatives for rough volatility with jumps," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(7), pages 1369-1406, July.
    20. Jiangze Du & Shaojie Lai & Kin Keung Lai & Shifei Zhou, 2021. "A novel term structure stochastic model with adaptive correlation for trend analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5485-5498, October.

    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:jotpro:v:35:y:2022:i:4:d:10.1007_s10959-022-01157-1. 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.