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Generalized Binomial Trees

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  • Jens Carsten Jackwerth.

Abstract

We consider the problem of consistently pricing new options given the prices of related options on the same stock. The Black-Scholes formula and standard binomial trees can only accommodate one related European option which then effectively specifies the volatility parameter. Implied binomial trees can accommodate only related European options with the same time-to-expiration. The generalized binomial trees introduced here can accommodate any kind of related options (European, American, or exotic) with different times-to-expiration.
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Suggested Citation

  • Jens Carsten Jackwerth., 1996. "Generalized Binomial Trees," Research Program in Finance Working Papers RPF-264, University of California at Berkeley.
    • Handle: RePEc:ucb:calbrf:rpf-264
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    References listed on IDEAS

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    1. Ronald Lagnado & Stanley Osher, "undated". "A Technique for Calibrating Derivative Security Pricing Models: Numerical Solution of an Inverse Problem," Computing in Economics and Finance 1997 101, Society for Computational Economics.
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    Cited by:

    1. Sonali Jain & Jayanth R. Varma & Sobhesh Kumar Agarwalla, 2019. "Indian equity options: Smile, risk premiums, and efficiency," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(2), pages 150-163, February.
    2. Tianyang Wang & James S. Dyer, 2010. "Valuing Multifactor Real Options Using an Implied Binomial Tree," Decision Analysis, INFORMS, vol. 7(2), pages 185-195, June.
    3. Shane Barratt & Jonathan Tuck & Stephen Boyd, 2020. "Convex Optimization Over Risk-Neutral Probabilities," Papers 2003.02878, arXiv.org.
    4. Jurczenko, Emmanuel & Maillet, Bertrand & Negrea, Bogdan, 2002. "Revisited multi-moment approximate option pricing models: a general comparison (Part 1)," LSE Research Online Documents on Economics 24950, London School of Economics and Political Science, LSE Library.
    5. Atul Chandra & Peter R. Hartley & Gopalan Nair, 2022. "Multiple Volatility Real Options Approach to Investment Decisions Under Uncertainty," Decision Analysis, INFORMS, vol. 19(2), pages 79-98, June.
    6. Zsembery, Levente, 2003. "A volatilitás előrejelzése és a visszaszámított modellek [Forecasting of volatility and implied models]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(6), pages 519-542.
    7. Vipul Kumar Singh, 2016. "Pricing and hedging competitiveness of the tree option pricing models: Evidence from India," Journal of Asset Management, Palgrave Macmillan, vol. 17(6), pages 453-475, October.
    8. Leif Andersen & Jesper Andreasen, 2000. "Jump-Diffusion Processes: Volatility Smile Fitting and Numerical Methods for Option Pricing," Review of Derivatives Research, Springer, vol. 4(3), pages 231-262, October.
    9. Silvia Muzzioli, 2010. "Towards a volatility index for the Italian stock market," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 10091, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    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. Silvia Muzzioli, 2013. "The Information Content of Option-Based Forecasts of Volatility: Evidence from the Italian Stock Market," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 3(01), pages 1-46.
    12. Christoffersen, Peter & Jacobs, Kris & Chang, Bo Young, 2013. "Forecasting with Option-Implied Information," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 581-656, Elsevier.
    13. Ahmed Loulit, 2004. "Approximating equity volatility," Working Papers CEB 04-028.RS, ULB -- Universite Libre de Bruxelles.
    14. Kim, In Joon & Park, Gun Youb, 2006. "An empirical comparison of implied tree models for KOSPI 200 index options," International Review of Economics & Finance, Elsevier, vol. 15(1), pages 52-71.
    15. Bogdan Negrea & Bertrand Maillet & Emmanuel Jurczenko, 2002. "Revisited Multi-moment Approximate Option," FMG Discussion Papers dp430, Financial Markets Group.
    16. U Hou Lok & Yuh‐Dauh Lyuu, 2020. "Efficient trinomial trees for local‐volatility models in pricing double‐barrier options," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(4), pages 556-574, April.
    17. Chris Charalambous & Nicos Christofides & Eleni D. Constantinide & Spiros H. Martzoukos, 2007. "Implied non-recombining trees and calibration for the volatility smile," Quantitative Finance, Taylor & Francis Journals, vol. 7(4), pages 459-472.
    18. Terry Marsh & Takao Kobayashi, 2000. "The Contributions of Professors Fischer Black, Robert Merton and Myron Scholes to the Financial Services Industry," International Review of Finance, International Review of Finance Ltd., vol. 1(4), pages 295-315, December.
    19. Jackwerth, Jens Carsten & Rubinstein, Mark, 2003. "Recovering Probabilities and Risk Aversion from Option Prices and Realized Returns," MPRA Paper 11638, University Library of Munich, Germany, revised 2004.
    20. Elyas Elyasiani & Silvia Muzzioli & Alessio Ruggieri, 2016. "Forecasting and pricing powers of option-implied tree models: Tranquil and volatile market conditions," Department of Economics 0099, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    21. Arturo Leccadito & Pietro Toscano & Radu S. Tunaru, 2012. "Hermite Binomial Trees: A Novel Technique For Derivatives Pricing," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 15(08), pages 1-36.
    22. Wael Bahsoun & Pawel Góra & Silvia Mayoral & Manuel Morales, 2006. "Random Dynamics and Finance: Constructing Implied Binomial Trees from a Predetermined Stationary Den," Faculty Working Papers 13/06, School of Economics and Business Administration, University of Navarra.
    23. Marco Avellaneda & Craig Friedman & Richard Holmes & Dominick Samperi, 1997. "Calibrating volatility surfaces via relative-entropy minimization," Applied Mathematical Finance, Taylor & Francis Journals, vol. 4(1), pages 37-64.
    24. Moriggia, V. & Muzzioli, S. & Torricelli, C., 2009. "On the no-arbitrage condition in option implied trees," European Journal of Operational Research, Elsevier, vol. 193(1), pages 212-221, February.
    25. Dasheng Ji & B. Brorsen, 2011. "A recombining lattice option pricing model that relaxes the assumption of lognormality," Review of Derivatives Research, Springer, vol. 14(3), pages 349-367, October.

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    JEL classification:

    • G19 - Financial Economics - - General Financial Markets - - - Other
    • G0 - Financial Economics - - General

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