IDEAS home Printed from https://ideas.repec.org/a/wly/jnlaaa/v2014y2014i1n259297.html

Pricing of Two Kinds of Power Options under Fractional Brownian Motion, Stochastic Rate, and Jump‐Diffusion Models

Author

Listed:
  • Kaili Xiang
  • Yindong Zhang
  • Xiaotong Mao

Abstract

Option pricing is always one of the critical issues in financial mathematics and economics. Brownian motion is the basic hypothesis of option pricing model, which questions the fractional property of stock price. In this paper, under the assumption that the exchange rate follows the extended Vasicek model, we obtain the closed form of the pricing formulas for two kinds of power options under fractional Brownian Motion (FBM) jump‐diffusion models.

Suggested Citation

  • Kaili Xiang & Yindong Zhang & Xiaotong Mao, 2014. "Pricing of Two Kinds of Power Options under Fractional Brownian Motion, Stochastic Rate, and Jump‐Diffusion Models," Abstract and Applied Analysis, John Wiley & Sons, vol. 2014(1).
  • Handle: RePEc:wly:jnlaaa:v:2014:y:2014:i:1:n:259297
    DOI: 10.1155/2014/259297
    as

    Download full text from publisher

    File URL: https://doi.org/10.1155/2014/259297
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2014/259297?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. Robert J. Elliott & John Van Der Hoek, 2003. "A General Fractional White Noise Theory And Applications To Finance," Mathematical Finance, Wiley Blackwell, vol. 13(2), pages 301-330, April.
    2. Robert Elliott & Leunglung Chan, 2004. "Perpetual American options with fractional Brownian motion," Quantitative Finance, Taylor & Francis Journals, vol. 4(2), pages 123-128.
    3. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    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. Wang, Xiao-Tian & Wu, Min & Zhou, Ze-Min & Jing, Wei-Shu, 2012. "Pricing European option with transaction costs under the fractional long memory stochastic volatility model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1469-1480.
    2. Dufera, Tamirat Temesgen, 2024. "Fractional Brownian motion in option pricing and dynamic delta hedging: Experimental simulations," The North American Journal of Economics and Finance, Elsevier, vol. 69(PB).
    3. Paula Morales-Bañuelos & Sebastian Elias Rodríguez Bojalil & Luis Alberto Quezada-Téllez & Guillermo Fernández-Anaya, 2025. "A General Conformable Black–Scholes Equation for Option Pricing," Mathematics, MDPI, vol. 13(10), pages 1-29, May.
    4. Xiao, Wei-Lin & Zhang, Wei-Guo & Zhang, Xili & Zhang, Xiaoli, 2012. "Pricing model for equity warrants in a mixed fractional Brownian environment and its algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(24), pages 6418-6431.
    5. Schadner, Wolfgang, 2020. "An idea of risk-neutral momentum and market fear," Finance Research Letters, Elsevier, vol. 37(C).
    6. Panhong Cheng & Zhihong Xu & Zexing Dai, 2023. "Valuation of vulnerable options with stochastic corporate liabilities in a mixed fractional Brownian motion environment," Mathematics and Financial Economics, Springer, volume 17, number 3, June.
    7. Taghipour, M. & Aminikhah, H., 2022. "A spectral collocation method based on fractional Pell functions for solving time–fractional Black–Scholes option pricing model," Chaos, Solitons & Fractals, Elsevier, vol. 163(C).
    8. Ha, Mijin & Kim, Donghyun & Yoon, Ji-Hun & Choi, Sun-Yong, 2025. "Pricing for perpetual American strangle options under stochastic volatility with fast mean reversion," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 227(C), pages 41-57.
    9. Stoyan V. Stoyanov & Svetlozar T. Rachev & Stefan Mittnik & Frank J. Fabozzi, 2019. "Pricing Derivatives In Hermite Markets," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(06), pages 1-27, September.
    10. Wang, Xiao-Tian, 2011. "Scaling and long-range dependence in option pricing V: Multiscaling hedging and implied volatility smiles under the fractional Black–Scholes model with transaction costs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(9), pages 1623-1634.
    11. Tebaldi, Claudio, 2005. "Hedging using simulation: a least squares approach," Journal of Economic Dynamics and Control, Elsevier, vol. 29(8), pages 1287-1312, August.
    12. R. Vilela Mendes, 2022. "The fractional volatility model and rough volatility," Papers 2206.02205, arXiv.org.
    13. Jian Pan & Xiangying Zhou, 2017. "Pricing for options in a mixed fractional Hull–White interest rate model," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 4(01), pages 1-15, March.
    14. Jamdee, Sutthisit & Los, Cornelis A., 2007. "Long memory options: LM evidence and simulations," Research in International Business and Finance, Elsevier, vol. 21(2), pages 260-280, June.
    15. Yan Zhang & Di Pan & Sheng-Wu Zhou & Miao Han, 2014. "Asian Option Pricing with Transaction Costs and Dividends under the Fractional Brownian Motion Model," Journal of Applied Mathematics, John Wiley & Sons, vol. 2014(1).
    16. Nikolai Leonenko & EStuart Petherick & Emanuele Taufer, 2012. "Multifractal Scaling for Risky Asset Modelling," DISA Working Papers 2012/07, Department of Computer and Management Sciences, University of Trento, Italy, revised Jul 2012.
    17. Yuecai Han & Xudong Zheng, 2022. "Approximate Pricing of Derivatives Under Fractional Stochastic Volatility Model," Papers 2210.15453, arXiv.org.
    18. Stoyan V. Stoyanov & Yong Shin Kim & Svetlozar T. Rachev & Frank J. Fabozzi, 2017. "Option pricing for Informed Traders," Papers 1711.09445, arXiv.org.
    19. Wolfgang Schadner & Sebastian Lang, 2023. "The value of expected return persistence," Annals of Finance, Springer, vol. 19(4), pages 449-476, December.
    20. Kazmerchuk, Yuriy & Swishchuk, Anatoliy & Wu, Jianhong, 2007. "The pricing of options for securities markets with delayed response," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 75(3), pages 69-79.

    More about this item

    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:wly:jnlaaa:v:2014:y:2014:i:1:n:259297. 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: Wiley Content Delivery (email available below). General contact details of provider: https://onlinelibrary.wiley.com/journal/4058 .

    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.