Abstract
The paper compares numerically the results from two real option valuation methods, the Datar-Mathews method and the fuzzy pay-off method. Datar-Mathews method is based on using Monte Carlo simulation within a probabilistic valuation framework, while the fuzzy pay-off method relies on modeling the real option valuation by using fuzzy numbers in a possibilistic space. The results show that real option valuation results from the two methods seem to be consistent with each other. The fuzzy pay-off method is more robust and is also usable when not enough information is available for a construction of a simulation model.
Highlights
Real option analysis (ROA) is slowly becoming a part of the investment analysis process in companies [1,2,3], while it has been gaining more and more attention in academia
The first models used for numerical real option valuation were models that had been originally designed for the valuation of financial options, namely, the Black-Scholes formula [6] and binomial option pricing techniques [7]
This paper concentrates on comparatively numerically analyzing two ROA methods, the Datar-Mathews method (DMM) that exploits Monte Carlo simulation in real option valuation [12,13,14] and the fuzzy pay-off method (FPOM) that is based on using managerially estimated cash-flow scenarios represented as fuzzy numbers as the basis for real option valuation [19,20,21,22]
Summary
Real option analysis (ROA) is slowly becoming a part of the investment analysis process in companies [1,2,3], while it has been gaining more and more attention in academia. This paper concentrates on comparatively numerically analyzing two ROA methods, the Datar-Mathews method (DMM) that exploits Monte Carlo simulation in real option valuation [12,13,14] and the fuzzy pay-off method (FPOM) that is based on using managerially estimated cash-flow scenarios represented as fuzzy numbers as the basis for real option valuation [19,20,21,22]. Both methods have similar real option valuation logic [31] but are based on a different set of modeling choices. The method has previously been used, for example, in the valuation of aircraft development projects [12, 13, 34], analysis of prognostic technology in health management [35], and the evaluation of renewable energy projects [36]
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