IDEAS home Printed from https://ideas.repec.org/a/taf/apfiec/v21y2011i10p747-754.html
   My bibliography  Save this article

Pricing Taiwan option market with GARCH and stochastic volatility

Author

Listed:
  • Hung-Hsi Huang
  • Ching-Ping Wang
  • Shiau-Hung Chen

Abstract

This study compares the out-of-sample performances among Black-Scholes (B-S), Stochastic Volatility (SV) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models in the Taiwan option market. Using Absolute Relative Pricing Error (ARPE) as the performance criterion, the empirical result reveals that the performance for GARCH is the best, and SV slightly dominates B-S. Additionally, this study performs the regression of ARPE on time-to-maturity, moneyness and a binary variable that is set to unity, if the option is a call and to zero in the case of a put. For the three models, the regression result displays that the pricing error is consistently decreasing in time-to-maturity and moneyness, and the out-of-sample performance in puts are more accurate than those in calls. Since the corresponding R2 of the regression in GARCH is the smallest, the pricing error for the other two models is relatively severe with respect to the three explanatory variables.

Suggested Citation

  • Hung-Hsi Huang & Ching-Ping Wang & Shiau-Hung Chen, 2011. "Pricing Taiwan option market with GARCH and stochastic volatility," Applied Financial Economics, Taylor & Francis Journals, vol. 21(10), pages 747-754.
    • Handle: RePEc:taf:apfiec:v:21:y:2011:i:10:p:747-754
    DOI: 10.1080/09603107.2010.535786
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/09603107.2010.535786
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/09603107.2010.535786?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jin-Chuan Duan & Jean-Guy Simonato, 1998. "Empirical Martingale Simulation for Asset Prices," Management Science, INFORMS, vol. 44(9), pages 1218-1233, September.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Hull, John C & White, Alan D, 1987. "The Pricing of Options on Assets with Stochastic Volatilities," Journal of Finance, American Finance Association, vol. 42(2), pages 281-300, June.
    4. Duan, Jin-Chuan & Zhang, Hua, 2001. "Pricing Hang Seng Index options around the Asian financial crisis - A GARCH approach," Journal of Banking & Finance, Elsevier, vol. 25(11), pages 1989-2014, November.
    5. Lehar, Alfred & Scheicher, Martin & Schittenkopf, Christian, 2002. "GARCH vs. stochastic volatility: Option pricing and risk management," Journal of Banking & Finance, Elsevier, vol. 26(2-3), pages 323-345, March.
    6. Bakshi, Gurdip & Cao, Charles & Chen, Zhiwu, 1997. "Empirical Performance of Alternative Option Pricing Models," Journal of Finance, American Finance Association, vol. 52(5), pages 2003-2049, December.
    7. repec:bla:jfinan:v:53:y:1998:i:6:p:2059-2106 is not listed on IDEAS
    8. 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. Aparna Bhat & Kirti Arekar, 2016. "Empirical Performance of Black-Scholes and GARCH Option Pricing Models during Turbulent Times: The Indian Evidence," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 8(3), pages 123-136, March.
    2. Wen-chung Guo & Ying-huei Chen, 2014. "Pricing of put warrants and competition among issuers," Economics Bulletin, AccessEcon, vol. 34(4), pages 2315-2323.

    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. Lin, Shin-Hung & Huang, Hung-Hsi & Li, Sheng-Han, 2015. "Option pricing under truncated Gram–Charlier expansion," The North American Journal of Economics and Finance, Elsevier, vol. 32(C), pages 77-97.
    2. Peter Christoffersen & Kris Jacobs & Chayawat Ornthanalai, 2012. "GARCH Option Valuation: Theory and Evidence," CREATES Research Papers 2012-50, Department of Economics and Business Economics, Aarhus University.
    3. 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.
    4. Peter Christoffersen & Kris Jacobs, 2004. "Which GARCH Model for Option Valuation?," Management Science, INFORMS, vol. 50(9), pages 1204-1221, September.
    5. Aparna Bhat & Kirti Arekar, 2016. "Empirical Performance of Black-Scholes and GARCH Option Pricing Models during Turbulent Times: The Indian Evidence," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 8(3), pages 123-136, March.
    6. Badescu, Alexandru & Elliott, Robert J. & Ortega, Juan-Pablo, 2014. "Quadratic hedging schemes for non-Gaussian GARCH models," Journal of Economic Dynamics and Control, Elsevier, vol. 42(C), pages 13-32.
    7. Lars Stentoft, 2008. "American Option Pricing Using GARCH Models and the Normal Inverse Gaussian Distribution," Journal of Financial Econometrics, Oxford University Press, vol. 6(4), pages 540-582, Fall.
    8. 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.
    9. Wong, Woon K. & Tu, Anthony H., 2009. "Market imperfections and the information content of implied and realized volatility," Pacific-Basin Finance Journal, Elsevier, vol. 17(1), pages 58-79, January.
    10. Gondzio, Jacek & Kouwenberg, Roy & Vorst, Ton, 2003. "Hedging options under transaction costs and stochastic volatility," Journal of Economic Dynamics and Control, Elsevier, vol. 27(6), pages 1045-1068, April.
    11. Badescu Alex & Kulperger Reg & Lazar Emese, 2008. "Option Valuation with Normal Mixture GARCH Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 12(2), pages 1-42, May.
    12. Bing-Huei Lin & Mao-Wei Hung & Jr-Yan Wang & Ping-Da Wu, 2013. "A lattice model for option pricing under GARCH-jump processes," Review of Derivatives Research, Springer, vol. 16(3), pages 295-329, October.
    13. Gilles Daniel & Nathan Joseph & David Bree, 2005. "Stochastic volatility and the goodness-of-fit of the Heston model," Quantitative Finance, Taylor & Francis Journals, vol. 5(2), pages 199-211.
    14. Joanna Górka, 2014. "Option Pricing under Sign RCA-GARCH Models," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 14, pages 145-160.
    15. Masato Ubukata & Toshiaki Watanabe, 2011. "Pricing Nikkei 225 Options Using Realized Volatility," IMES Discussion Paper Series 11-E-18, Institute for Monetary and Economic Studies, Bank of Japan.
    16. 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.
    17. Stentoft, Lars, 2011. "American option pricing with discrete and continuous time models: An empirical comparison," Journal of Empirical Finance, Elsevier, vol. 18(5), pages 880-902.
    18. Matthias R. Fengler & Alexander Melnikov, 2018. "GARCH option pricing models with Meixner innovations," Review of Derivatives Research, Springer, vol. 21(3), pages 277-305, October.
    19. Liu, Chang & Chang, Chuo, 2021. "Combination of transition probability distribution and stable Lorentz distribution in stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    20. Capelle-Blancard, Gunther & Jurczenko, Emmanuel & Maillet, Bertrand, 2001. "The approximate option pricing model: performances and dynamic properties," Journal of Multinational Financial Management, Elsevier, vol. 11(4-5), pages 427-443, December.

    More about this item

    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:taf:apfiec:v:21:y:2011:i:10:p:747-754. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAFE20 .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.