Abstract
In this paper we study the robustness 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 option 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, stress testing different basis functions, we show the major technical advantages as reduction of the execution time and improvement in the simulation on the R&D projects valuation.
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Villani, G.: Valuation of R&D investment opportunities using the least-squares Monte Carlo method. In: Corazza, M., Pizzi, C. (eds.) Mathematical and Statistical Methods for Actuarial Sciences and Finance, pp. 287–299. Springer, Basel (2014)
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Biancardi, M., Villani, G. (2014). A Robustness Analysis of Least-Squares Monte Carlo for R&D Real Options Valuation. In: Perna, C., Sibillo, M. (eds) Mathematical and Statistical Methods for Actuarial Sciences and Finance. Springer, Cham. https://doi.org/10.1007/978-3-319-05014-0_6
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DOI: https://doi.org/10.1007/978-3-319-05014-0_6
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05013-3
Online ISBN: 978-3-319-05014-0
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