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A simulation-based algorithm for American executive stock option valuation

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  • León, Angel
  • Vaello-Sebastià, Antoni

Abstract

We present an algorithm that merges a certainty-equivalence framework with the least-squares Monte Carlo algorithm to obtain the executive stock option (ESO) value for a risk-averse and undiversified agent. We account for the difference between executive's value and firm cost of the ESO. We show how early-exercise decisions depend on executive's preferences and its diversification degree. Because of the algorithm flexibility, it allows for multiple state-variables. As an example, we consider the case of indexed ESOs revealing a significant improvement in terms of executive's discount respect to fixed strike ESOs.

Suggested Citation

  • León, Angel & Vaello-Sebastià, Antoni, 2010. "A simulation-based algorithm for American executive stock option valuation," Finance Research Letters, Elsevier, vol. 7(1), pages 14-23, March.
    • Handle: RePEc:eee:finlet:v:7:y:2010:i:1:p:14-23
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    References listed on IDEAS

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    Cited by:

    1. Susana Alvarez-Diez & J. Samuel Baixauli-Soler & Maria Belda-Ruiz, 2016. "Early Exercise Behaviour in Performance-vested Stock Option Grants," Annals of Economics and Finance, Society for AEF, vol. 17(1), pages 55-78, May.
    2. Carmona, Julio & León, Angel & Vaello-Sebastià, Antoni, 2012. "Does stock return predictability affect ESO fair value?," European Journal of Operational Research, Elsevier, vol. 223(1), pages 188-202.

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