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Generalized Cox-Ross-Rubinstein Binomial Models

Author

Listed:
  • San-Lin Chung

    (Department of Finance, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan)

  • Pai-Ta Shih

    (Department of Economics, National Dong-Hwa University, No. 1, Sec. 2, Da Hsueh Road, Shoufeng, Hualien 97401, Taiwan)

Abstract

This paper generalizes the seminal Cox-Ross-Rubinstein (CRR) binomial model by adding a stretch parameter. The generalized CRR (GCRR) model allows us to fine-tune (via the stretch parameter) the lattice structure so as to efficiently price a range of options, such as barrier options. Our analysis provides insights into the fine structure of convergence of the general binomial model to the Black-Scholes formula. We also discuss how to improve the rate of convergence or the oscillatory behavior of the GCRR model. The numerical results suggest that the GCRR models with various modifications are efficient for pricing a range of options.

Suggested Citation

  • San-Lin Chung & Pai-Ta Shih, 2007. "Generalized Cox-Ross-Rubinstein Binomial Models," Management Science, INFORMS, vol. 53(3), pages 508-520, March.
    • Handle: RePEc:inm:ormnsc:v:53:y:2007:i:3:p:508-520
    DOI: 10.1287/mnsc.1060.0652
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    References listed on IDEAS

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    1. Leisen, Dietmar P. J., 1998. "Pricing the American put option: A detailed convergence analysis for binomial models," Journal of Economic Dynamics and Control, Elsevier, vol. 22(8-9), pages 1419-1444, August.
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    Citations

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    Cited by:

    1. Vidal Nunes, João Pedro & Ruas, João Pedro & Dias, José Carlos, 2015. "Pricing and static hedging of American-style knock-in options on defaultable stocks," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 343-360.
    2. Shvimer, Yossi & Herbon, Avi, 2020. "Comparative empirical study of binomial call-option pricing methods using S&P 500 index data," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    3. Jean-Christophe Breton & Youssef El-Khatib & Jun Fan & Nicolas Privault, 2021. "A q-binomial extension of the CRR asset pricing model," Papers 2104.10163, arXiv.org, revised Feb 2023.
    4. Chung, San-Lin & Shih, Pai-Ta, 2009. "Static hedging and pricing American options," Journal of Banking & Finance, Elsevier, vol. 33(11), pages 2140-2149, November.
    5. Chung, San-Lin & Shih, Pai-Ta & Tsai, Wei-Che, 2013. "Static hedging and pricing American knock-in put options," Journal of Banking & Finance, Elsevier, vol. 37(1), pages 191-205.
    6. Chung-Li Tseng & Daniel Wei-Chung Miao & San-Lin Chung & Pai-Ta Shih, 2021. "How Much Do Negative Probabilities Matter in Option Pricing?: A Case of a Lattice-Based Approach for Stochastic Volatility Models," JRFM, MDPI, vol. 14(6), pages 1-32, May.
    7. Glazyrina, Anna & Melnikov, Alexander, 2016. "Bernstein’s inequalities and their extensions for getting the Black–Scholes option pricing formula," Statistics & Probability Letters, Elsevier, vol. 111(C), pages 86-92.
    8. Warren J. Hahn & James S. Dyer, 2011. "A Discrete Time Approach for Modeling Two-Factor Mean-Reverting Stochastic Processes," Decision Analysis, INFORMS, vol. 8(3), pages 220-232, September.
    9. Aricson Cruz & José Carlos Dias, 2020. "Valuing American-style options under the CEV model: an integral representation based method," Review of Derivatives Research, Springer, vol. 23(1), pages 63-83, April.
    10. Lo, Chien-Ling & Shih, Pai-Ta & Wang, Yaw-Huei & Yu, Min-Teh, 2019. "VIX derivatives: Valuation models and empirical evidence," Pacific-Basin Finance Journal, Elsevier, vol. 53(C), pages 1-21.
    11. Tianyang Wang & James Dyer & Warren Hahn, 2015. "A copula-based approach for generating lattices," Review of Derivatives Research, Springer, vol. 18(3), pages 263-289, October.

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