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A simulation-based algorithm for American executive stock option valuation
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DOI：10.1016/j.frl.2009.11.001.png)
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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. (C) 2009 Elsevier Inc. All rights reserved.
Keyword:
Executive stock options
Monte Carlo
Risk aversion
Fair value
Indexed strike
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Exercise behavior, valuation, and the incentive effects of employee stock options行权行为、估值与员工股票期权的激励效应
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