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Executive Stock Option Pricing in China under Stochastic Volatility

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  • Chong, Terence Tai Leung
  • Ding, Yue
  • Li, Yong

Abstract

In this paper, on the basis of stochastic volatility (SV) models, we extend the approach of option pricing for executive stock options (ESOs) under FAS 123. Based on this extension, a sample of Chinese listed companies’ ESOs are priced. We analyze the effect of the some important financial variables on the implementation of ESOs. It is found that in China, firms with higher market risk and larger size are likely to have a higher ESO proportion in their executive incentive plans. The effects of the book-to market ratio, stock price volatility, executive shareholding proportion, and the leverage ratio are also examined.

Suggested Citation

  • Chong, Terence Tai Leung & Ding, Yue & Li, Yong, 2015. "Executive Stock Option Pricing in China under Stochastic Volatility," MPRA Paper 63397, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:63397
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    References listed on IDEAS

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    1. Choe, Chongwoo, 2003. "Leverage, volatility and executive stock options," Journal of Corporate Finance, Elsevier, vol. 9(5), pages 591-609, November.
    2. Cuny, Charles J. & Jorion, Philippe, 1995. "Valuing executive stock options with endogenous departure," Journal of Accounting and Economics, Elsevier, vol. 20(2), pages 193-205, September.
    3. Mehran, Hamid, 1995. "Executive compensation structure, ownership, and firm performance," Journal of Financial Economics, Elsevier, vol. 38(2), pages 163-184, June.
    4. Paul L. Joskow & Nancy L. Rose & Catherine Wolfram, 1996. "Political Constraints on Executive Compensation: Evidence from the Electric Utility Industry," RAND Journal of Economics, The RAND Corporation, vol. 27(1), pages 165-182, Spring.
    5. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," Review of Economic Studies, Oxford University Press, vol. 65(3), pages 361-393.
    6. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    7. Chen, Yenn-Ru & Lee, Bong Soo, 2010. "A dynamic analysis of executive stock options: Determinants and consequences," Journal of Corporate Finance, Elsevier, vol. 16(1), pages 88-103, February.
    8. Yermack, David, 1995. "Do corporations award CEO stock options effectively?," Journal of Financial Economics, Elsevier, vol. 39(2-3), pages 237-269.
    9. Jin‐Chuan Duan, 1995. "The Garch Option Pricing Model," Mathematical Finance, Wiley Blackwell, vol. 5(1), pages 13-32, January.
    10. Renate Meyer & Jun Yu, 2000. "BUGS for a Bayesian analysis of stochastic volatility models," Econometrics Journal, Royal Economic Society, vol. 3(2), pages 198-215.
    11. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Cited by:

    1. Zhiwei Su & Xingchun Wang, 2019. "Pricing executive stock options with averaging features under the Heston–Nandi GARCH model," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(9), pages 1056-1084, September.
    2. Wang, Xingchun, 2018. "Valuing executive stock options under correlated employment shocks," Finance Research Letters, Elsevier, vol. 27(C), pages 38-45.

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    More about this item

    Keywords

    Bayesian analysis; Executive stock options; FAS 123; Option pricing; SV models.;

    JEL classification:

    • G3 - Financial Economics - - Corporate Finance and Governance

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