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

In: Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author

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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)

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.

Suggested Citation

  • Marta Biancardi & Giovanni Villani, 2014. "A Robustness Analysis of Least-Squares Monte Carlo for R&D Real Options Valuation," Springer Books, in: Cira Perna & Marilena Sibillo (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, edition 127, pages 27-30, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-05014-0_6
    DOI: 10.1007/978-3-319-05014-0_6
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