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Valuing Multifactor Real Options Using an Implied Binomial Tree

Author

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  • Tianyang Wang

    (McCombs School of Business, The University of Texas at Austin, Austin, Texas 78712)

  • James S. Dyer

    (McCombs School of Business, The University of Texas at Austin, Austin, Texas 78712)

Abstract

This paper proposes an approach for solving a multifactor real options problem by approximating the underlying stochastic process with an implied binomial tree. The implied binomial tree is constructed to be consistent with simulated market information. By simulating European option prices as artificial market information, we apply the implied binomial tree method for real options valuation when the options are contingent on the value of market uncertainties that are not traded assets. Compared to the discrete approximations suggested in the current literature, this method offers a more flexible distribution assumption for project values and therefore provides an alternative approach to estimating the value of high-dimensional real options. For risk managers, it serves as a capital budgeting method for projects with managerial flexibility.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:ordeca:v:7:y:2010:i:2:p:185-195
    DOI: 10.1287/deca.1100.0174
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Jing Ai & Patrick L. Brockett & Tianyang Wang, 2017. "Optimal Enterprise Risk Management and Decision Making With Shared and Dependent Risks," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 84(4), pages 1127-1169, December.
    3. Alejandro Mac Cawley & Maximiliano Cubillos & Rodrigo Pascual, 2020. "A real options approach for joint overhaul and replacement strategies with mean reverting prices," Annals of Operations Research, Springer, vol. 286(1), pages 303-324, March.
    4. Tianyang Wang & James S. Dyer, 2012. "A Copulas-Based Approach to Modeling Dependence in Decision Trees," Operations Research, INFORMS, vol. 60(1), pages 225-242, February.
    5. Pedro Godinho, 2015. "Estimating State-Dependent Volatility of Investment Projects: A Simulation Approach," GEMF Working Papers 2015-02, GEMF, Faculty of Economics, University of Coimbra.
    6. L. Robin Keller, 2011. "From the Editor ---Multiattribute and Intertemporal Preferences, Probability, and Stochastic Processes: Models and Assessment," Decision Analysis, INFORMS, vol. 8(3), pages 165-169, September.
    7. Seiji Harikae & James S. Dyer & Tianyang Wang, 2021. "Valuing Real Options in the Volatile Real World," Production and Operations Management, Production and Operations Management Society, vol. 30(1), pages 171-189, January.
    8. Nadarajah, Selvaprabu & Margot, François & Secomandi, Nicola, 2017. "Comparison of least squares Monte Carlo methods with applications to energy real options," European Journal of Operational Research, Elsevier, vol. 256(1), pages 196-204.
    9. L. Robin Keller & Kelly M. Kophazi, 2010. "From the Editors..," Decision Analysis, INFORMS, vol. 7(2), pages 151-154, June.

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