Robust Monte Carlo Method for R&D Real Options Valuation

Marta Biancardi () and Giovanni Villani ()
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Marta Biancardi: University of Foggia
Giovanni Villani: University of Bari

Computational Economics, 2017, vol. 49, issue 3, No 7, 498 pages

Abstract: Abstract This paper is devoted to developing a robust numerical analysis of least squares Monte Carlo (LSM) in valuing R&D investment opportunities. As it is well known, R&D projects are characterized by sequential investments and therefore they can be considered as compound options involving a set of interacting American-type options. The basic Monte Carlo simulation takes a long time and it is computationally intensive and inefficient. In this context, LSM method is a powerful and flexible tool for capital budgeting decisions and for valuing R&D investments. In particular way, numerical tests are performed to examine the optimal choice of basis function and polynomial degree in terms of reduction of the execution time, accuracy and improvement in the simulation.

Keywords: Least-squares Monte Carlo; R&D real options; Robustness analysis (search for similar items in EconPapers)
JEL-codes: C15 C63 G13 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10614-016-9578-z

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