IDEAS home Printed from https://ideas.repec.org/p/wpa/wuwpfi/0405032.html
   My bibliography  Save this paper

GARCH Option Pricing Under Skew

Author

Listed:
  • Sofiane ABOURA

    (ESSEC Busienss School)

Abstract

This article is an empirical study dedicated to the GARCH Option pricing model of Duan (1995) applied to the FTSE 100 European style options for various maturities. The beauty of this model is to have used the standard GARCH theory in an option perspective and also it is its flexibility to adapt to different rich GARCH specifications. We analyze the valididy of the model given its ability to price one-day ahead out- of-sample call options and also its ability to capture the empirical dynamic of the volatility skew. We get severe mispricing for deep out- of-the-money and short term call options, which tend to decrease the global performance of the model that is relatively correct. We note that long term skews tend to be more stable across time and strikes, which explains why we had a decreasing pricing bias for longer maturity contracts. We also get that skews tend to deform into smiles as we go toward the expiry date. This model reveals a good ability to capture the change of regime in the implied volatility surface judging from the transformation observed from smiles to skews.

Suggested Citation

  • Sofiane ABOURA, 2004. "GARCH Option Pricing Under Skew," Finance 0405032, University Library of Munich, Germany.
  • Handle: RePEc:wpa:wuwpfi:0405032
    Note: Type of Document - pdf; pages: 14
    as

    Download full text from publisher

    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/fin/papers/0405/0405032.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    2. Blair, Bevan J. & Poon, Ser-Huang & Taylor, Stephen J., 2001. "Forecasting S&P 100 volatility: the incremental information content of implied volatilities and high-frequency index returns," Journal of Econometrics, Elsevier, vol. 105(1), pages 5-26, November.
    3. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    4. Christensen, B. J. & Prabhala, N. R., 1998. "The relation between implied and realized volatility," Journal of Financial Economics, Elsevier, vol. 50(2), pages 125-150, November.
    Full references (including those not matched with items on IDEAS)

    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. Bali, Turan G. & Weinbaum, David, 2007. "A conditional extreme value volatility estimator based on high-frequency returns," Journal of Economic Dynamics and Control, Elsevier, vol. 31(2), pages 361-397, February.
    2. Benavides Guillermo, 2006. "Volatility Forecasts for the Mexican Peso - U.S. Dollar Exchange Rate: An Empirical Analysis of Garch, Option Implied and Composite Forecast Models," Working Papers 2006-04, Banco de México.
    3. repec:dau:papers:123456789/2138 is not listed on IDEAS
    4. Benavides, Guillermo, 2009. "Predictive Accuracy of Futures Options Implied Volatility: the Case of the Exchange Rate Futures Mexican Peso-Us Dollar," Panorama Económico, Escuela Superior de Economía, Instituto Politécnico Nacional, vol. 0(09), pages 55-95, segundo s.
    5. GIOT, Pierre, 2003. "The information content of implied volatility indexes for forecasting volatility and market risk," LIDAM Discussion Papers CORE 2003027, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    6. Christoffersen, Peter & Jacobs, Kris & Chang, Bo Young, 2013. "Forecasting with Option-Implied Information," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 581-656, Elsevier.
    7. Koopman, Siem Jan & Jungbacker, Borus & Hol, Eugenie, 2005. "Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements," Journal of Empirical Finance, Elsevier, vol. 12(3), pages 445-475, June.
    8. Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil price realized volatility using information channels from other asset classes," Journal of International Money and Finance, Elsevier, vol. 76(C), pages 28-49.
    9. Stavros Degiannakis, George Filis, and Renatas Kizys, 2014. "The Effects of Oil Price Shocks on Stock Market Volatility: Evidence from European Data," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    10. Bent Jesper Christensen & Morten Ø. Nielsen, 2005. "The Implied-realized Volatility Relation With Jumps In Underlying Asset Prices," Working Paper 1186, Economics Department, Queen's University.
    11. Gael M. Martin & Andrew Reidy & Jill Wright, 2009. "Does the option market produce superior forecasts of noise-corrected volatility measures?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(1), pages 77-104.
    12. Supachok Thakolsri & Yuthana Sethapramote & Komain Jiranyakul, 2016. "Implied Volatility Transmissions Between Thai and Selected Advanced Stock Markets," SAGE Open, , vol. 6(3), pages 21582440166, July.
    13. Jaesun Noh & Tae-Hwan Kim, 2006. "Forecasting volatility of futures market: the S&P 500 and FTSE 100 futures using high frequency returns and implied volatility," Applied Economics, Taylor & Francis Journals, vol. 38(4), pages 395-413.
    14. Vogel, Harold L. & Werner, Richard A., 2015. "An analytical review of volatility metrics for bubbles and crashes," International Review of Financial Analysis, Elsevier, vol. 38(C), pages 15-28.
    15. Rosenberg, Joshua V. & Engle, Robert F., 2002. "Empirical pricing kernels," Journal of Financial Economics, Elsevier, vol. 64(3), pages 341-372, June.
    16. Chernov, Mikhail, 2007. "On the Role of Risk Premia in Volatility Forecasting," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 411-426, October.
    17. Stavros Degiannakis & George Filis & Renatas Kizys, 2013. "Oil price shocks and stock market volatility: evidence from European data," Working Papers 161, Bank of Greece.
    18. Mark R. Manfredo & Dwight R. Sanders, 2004. "The forecasting performance of implied volatility from live cattle options contracts: Implications for agribusiness risk management," Agribusiness, John Wiley & Sons, Ltd., vol. 20(2), pages 217-230.
    19. Mircea ASANDULUI, 2012. "A Multi-Horizon Comparison Of Volatility Forecasts: An Application To Stock Options Traded At Euronext Exchange Amsterdam," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 10, pages 179-190, December.
    20. Rossi, Alessandro & Gallo, Giampiero M., 2006. "Volatility estimation via hidden Markov models," Journal of Empirical Finance, Elsevier, vol. 13(2), pages 203-230, March.
    21. R. Glen Donaldson & Mark J. Kamstra, 2005. "Volatility Forecasts, Trading Volume, And The Arch Versus Option‐Implied Volatility Trade‐Off," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 28(4), pages 519-538, December.

    More about this item

    Keywords

    GARCH Option models; Monte Carlo simulations; Implied Volatility; Volatility Smile.;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

    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:wpa:wuwpfi:0405032. 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: EconWPA (email available below). General contact details of provider: https://econwpa.ub.uni-muenchen.de .

    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.