A Robustness Analysis of Least-Squares Monte Carlo for R&D Real Options Valuation

Marta Biancardi () and Giovanni Villani ()
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Marta Biancardi: Largo Papa Giovanni Paolo II 1, Department of Economics
Giovanni Villani: Largo Papa Giovanni Paolo II 1, Department of Economics

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2014, pp 27-30 from Springer

Abstract: 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.

Keywords: Least-squares Monte Carlo; R&D real options; Robustness analysis (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-05014-0_6

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DOI: 10.1007/978-3-319-05014-0_6

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