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Non-transferable non-hedgeable executive stock option pricing

Author

Listed:
  • Colwell, David B.
  • Feldman, David
  • Hu, Wei

Abstract

To value non-transferable non-hedgeable (NTNH) contingent claims and price executive stock options (ESOs), we use a replication argument to translate portfolios with NTNH derivatives into portfolios of primary assets (only) with stochastic portfolio constraints. By identifying stochastic discount factors and finding subjective prices of NTNH European and American ESOs, for block and continuous partial exercise, we derive executives׳ optimal exercise policies, and use these to find objective prices/costs of ESOs to firms. Through numerical simulations, we obtain policy implications regarding ESOs׳ incentivizing efficiency. For the first time, we demonstrate that, unlike under block exercise, subjective prices under continuous partial exercise may be higher than objective ones. Moreover, volatility regimes and executives׳ “other wealth” are important in ESO pricing, and are thus essential to empirical executive compensation studies.

Suggested Citation

  • Colwell, David B. & Feldman, David & Hu, Wei, 2015. "Non-transferable non-hedgeable executive stock option pricing," Journal of Economic Dynamics and Control, Elsevier, vol. 53(C), pages 161-191.
    • Handle: RePEc:eee:dyncon:v:53:y:2015:i:c:p:161-191
    DOI: 10.1016/j.jedc.2015.02.002
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    References listed on IDEAS

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    More about this item

    Keywords

    Executive stock options; Constrained portfolio optimization; Stochastic discount factor; Non-hedgeable; Non-transferable;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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