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Real Options with Random Controls and the Value of Learning

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  • Spiros Martzoukos

Abstract

In this paper we propose a conceptual framework for continuous-time valuation of real (investment) options in the presence of costly controls with random outcomes (learning), that affect the value of the underlying asset or a relevant state-variable. These controls represent optional efforts by management to add value to the underlying real investments over which it has monopoly power, albeit with uncertain results. Special cases of such controls include pure learning (but costly) actions, as in many research and development, marketing research or natural resource exploration projects. We demonstrate a discrete-time Markov-chain solution methodology implemented in a finite-difference scheme, and we discuss numerical results. The impact of such uncertain jumps is seen to be relatively more significant in the case of non-profitable options than in the case of very profitable real (investment) options. When the potential for information revelation is significant, we are even willing to pay for an action with a negative expected outcome. With numerical simulations we capture the value of embedded exploration (pure learning) options and we demonstrate the improvement over the traditional (sequential/compound) real options approach. We show that such exploration options enhance the value of investment opportunities in the most significant manner, and justify the (mostly unexplained) observed practice of “overpaying” for the purchase of rights to natural resources extraction. Copyright Kluwer Academic Publishers 2000

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  • Spiros Martzoukos, 2000. "Real Options with Random Controls and the Value of Learning," Annals of Operations Research, Springer, vol. 99(1), pages 305-323, December.
    • Handle: RePEc:spr:annopr:v:99:y:2000:i:1:p:305-323:10.1023/a:1019232102666
    DOI: 10.1023/A:1019232102666
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    Citations

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    Cited by:

    1. S H Martzoukos, 2009. "Real R&D options and optimal activation of two-dimensional random controls," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(6), pages 843-858, June.
    2. Nicos Koussis & Spiros Martzoukos & Lenos Trigeorgis, 2007. "Real R&D options with time-to-learn and learning-by-doing," Annals of Operations Research, Springer, vol. 151(1), pages 29-55, April.
    3. Alcino Azevedo & Dean Paxson, 2018. "Rivalry and uncertainty in complementary investments with dynamic market sharing," Annals of Operations Research, Springer, vol. 271(2), pages 319-355, December.
    4. Koussis, Nicos & Martzoukos, Spiros H. & Trigeorgis, Lenos, 2013. "Multi-stage product development with exploration, value-enhancing, preemptive and innovation options," Journal of Banking & Finance, Elsevier, vol. 37(1), pages 174-190.
    5. F. Wu & H. Li & L. Chu & D. Sculli & K. Gao, 2009. "An approach to the valuation and decision of ERP investment projects based on real options," Annals of Operations Research, Springer, vol. 168(1), pages 181-203, April.
    6. Haejun Jeon, 2023. "Time-to-build and capacity expansion," Annals of Operations Research, Springer, vol. 328(2), pages 1461-1494, September.
    7. Ana María Sánchez Pérez & Jorge Tarifa Fernández & Salvador Cruz Rambaud, 2020. "Assessing Blockchain Investments through the Learning Option: An Application to the Automotive and Aerospace Industry," Mathematics, MDPI, vol. 8(12), pages 1-13, December.
    8. Lukas, Elmar, 2013. "Modeling the transitional dynamics of international joint venture policies: An option pricing approach," International Review of Economics & Finance, Elsevier, vol. 27(C), pages 21-36.

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