IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/63397.html
   My bibliography  Save this paper

Executive Stock Option Pricing in China under Stochastic Volatility

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/63397/1/MPRA_paper_63397.pdf
    File Function: original version
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Font, Begoña, 1998. "Modelización de series temporales financieras. Una recopilación," DES - Documentos de Trabajo. Estadística y Econometría. DS 3664, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Yu, Jun & Yang, Zhenlin & Zhang, Xibin, 2006. "A class of nonlinear stochastic volatility models and its implications for pricing currency options," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2218-2231, December.
    3. Lengua Lafosse, Patricia & Rodríguez, Gabriel, 2018. "An empirical application of a stochastic volatility model with GH skew Student's t-distribution to the volatility of Latin-American stock returns," The Quarterly Review of Economics and Finance, Elsevier, vol. 69(C), pages 155-173.
    4. repec:zbw:cfswop:wp200508 is not listed on IDEAS
    5. M. Hakan Eratalay, 2016. "Estimation of Multivariate Stochastic Volatility Models: A Comparative Monte Carlo Study," International Econometric Review (IER), Econometric Research Association, vol. 8(2), pages 19-52, September.
    6. António Alberto Santos, 2015. "The evolution of the Volatility in Financial Returns: Realized Volatility vs Stochastic Volatility Measures," GEMF Working Papers 2015-10, GEMF, Faculty of Economics, University of Coimbra.
    7. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005. "Volatility Forecasting," PIER Working Paper Archive 05-011, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    8. Patricia Lengua & Cristian Bayes & Gabriel Rodríguez, 2015. "A Stochastic Volatility Model with GH Skew Student’s t-Distribution: Application to Latin-American Stock Returns," Documentos de Trabajo / Working Papers 2015-405, Departamento de Economía - Pontificia Universidad Católica del Perú.
    9. Ying Wang & Sai Tsang Boris Choy & Hoi Ying Wong, 2016. "Bayesian Option Pricing Framework with Stochastic Volatility for FX Data," Risks, MDPI, Open Access Journal, vol. 4(4), pages 1-1, December.
    10. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.),Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
    11. Chang, Chia-Lin & Hsu, Hui-Kuang, 2013. "Modelling Volatility Size Effects for Firm Performance: The Impact of Chinese Tourists to Taiwan," MPRA Paper 45691, University Library of Munich, Germany.
    12. Athanasia Gavala & Nikolay Gospodinov & Deming Jiang, 2006. "Forecasting volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(6), pages 381-400.
    13. Stentoft, Lars, 2005. "Pricing American options when the underlying asset follows GARCH processes," Journal of Empirical Finance, Elsevier, vol. 12(4), pages 576-611, September.
    14. Yu, Jun, 2012. "A semiparametric stochastic volatility model," Journal of Econometrics, Elsevier, vol. 167(2), pages 473-482.
    15. Joshua Chan & Arnaud Doucet & Roberto León-González & Rodney W. Strachan, 2018. "Multivariate Stochastic Volatility with Co-Heteroscedasticity," Working Paper series 18-38, Rimini Centre for Economic Analysis.
    16. Kam Fong Chan & Christopher Gan & Patricia A. McGraw, 2003. "A Hedging Strategy for New Zealand’s Exporters in Transaction Exposure to Currency Risk," Multinational Finance Journal, Multinational Finance Journal, vol. 7(1-2), pages 25-54, March-Jun.
    17. Pelletier, Denis, 2006. "Regime switching for dynamic correlations," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 445-473.
    18. Enrique Ter Horst & Abel Rodriguez & Henryk Gzyl & German Molina, 2012. "Stochastic volatility models including open, close, high and low prices," Quantitative Finance, Taylor & Francis Journals, vol. 12(2), pages 199-212, May.
    19. Gabriele Fiorentini & Enrique Sentana & Neil Shephard, 2004. "Likelihood-Based Estimation of Latent Generalized ARCH Structures," Econometrica, Econometric Society, vol. 72(5), pages 1481-1517, September.
    20. Tseng, Chih-Hsiung & Cheng, Sheng-Tzong & Wang, Yi-Hsien & Peng, Jin-Tang, 2008. "Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(13), pages 3192-3200.
    21. Eric Ghysels & Andrew Harvey & Éric Renault, 1995. "Stochastic Volatility," CIRANO Working Papers 95s-49, CIRANO.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:63397. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Joachim Winter). General contact details of provider: http://edirc.repec.org/data/vfmunde.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.