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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|Titolo:||A Robustness Analysis of Least-Squares Monte Carlo for R&D Real Options Valuation|
|Data di pubblicazione:||2014|
|Appare nelle tipologie:||2.1 Contributo in volume (Capitolo o Saggio)|