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Robust Monte Carlo Method for R&D Real Options Valuation

Author

Listed:
  • Marta Biancardi

    (University of Foggia)

  • Giovanni Villani

    (University of Bari)

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.

Suggested Citation

  • Marta Biancardi & Giovanni Villani, 2017. "Robust Monte Carlo Method for R&D Real Options Valuation," Computational Economics, Springer;Society for Computational Economics, vol. 49(3), pages 481-498, March.
    • Handle: RePEc:kap:compec:v:49:y:2017:i:3:d:10.1007_s10614-016-9578-z
    DOI: 10.1007/s10614-016-9578-z
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    References listed on IDEAS

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    1. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," University of California at Los Angeles, Anderson Graduate School of Management qt43n1k4jb, Anderson Graduate School of Management, UCLA.
    2. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," The Review of Financial Studies, Society for Financial Studies, vol. 14(1), pages 113-147.
    3. Rainer Andergassen & Luigi Sereno, 2012. "Valuation of N-stage Investments Under Jump-Diffusion Processes," Computational Economics, Springer;Society for Computational Economics, vol. 39(3), pages 289-313, March.
    4. Jinqiang Yang & Zhaojun Yang, 2012. "Consumption Utility-Based Pricing and Timing of the Option to Invest with Partial Information," Computational Economics, Springer;Society for Computational Economics, vol. 39(2), pages 195-217, February.
    5. Cassimon, Danny & Engelen, Peter-Jan & Yordanov, Vilimir, 2011. "Compound Real Option Valuation with Phase-Specific Volatility: a Multi-phase Mobile Payments Case Study," MPRA Paper 46053, University Library of Munich, Germany.
    6. Manuel Moreno & Javier Navas, 2003. "On the Robustness of Least-Squares Monte Carlo (LSM) for Pricing American Derivatives," Review of Derivatives Research, Springer, vol. 6(2), pages 107-128, May.
    7. Philip Protter & Emmanuelle Clément & Damien Lamberton, 2002. "An analysis of a least squares regression method for American option pricing," Finance and Stochastics, Springer, vol. 6(4), pages 449-471.
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    Cited by:

    1. Wei Wu & Boqiang Lin, 2020. "Reducing Overcapacity in China’s Coal Industry: A Real Option Approach," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1073-1093, April.
    2. Pierre Rostan & Alexandra Rostan & François-Éric Racicot, 2020. "Increment Variance Reduction Techniques with an Application to Multi-name Credit Derivatives," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 1-35, January.
    3. Zhao, Pingping & Wang, Tong & Song, Aimin & Chen, Peimin, 2023. "Valuing new drug R&D project under economic fluctuation, technical risks and subjective uncertainty," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).

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    More about this item

    Keywords

    Least-squares Monte Carlo; R&D real options; Robustness analysis;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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