IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/42193.html
   My bibliography  Save this paper

On the predictive power of implied volatility indexes: A comparative analysis with GARCH forecasted volatility

Author

Listed:
  • Bentes, Sonia R
  • Menezes, Rui

Abstract

This paper examines the behavior of several implied volatility indexes in order to compare them with the volatility forecasts obtained from estimating a GARCH model. Though volatility has always been a prevailing subject of research it has become particularly relevant given the increasingly complexity and uncertainty of stock markets in these days. An important measure to assess the market expectations of the future volatility of the underlying asset is the implied volatility (IV) indexes. Generally, these indexes are calculated based on the prices of out-of-the money put and call options on the underlying asset. Sometimes called the “investor fear gauge”, the IV indexes are a measure of the implied volatility of the underlying index. This study focuses on the implied and GARCH forecasted volatility of some emerging countries and some developed countries. More specifically, it compares the predictive power of the IV indexes with the ones provided by standard volatility models such as the ARCH/GARCH (Autoregressive Conditional Heteroskedasticity Model/ Generalized Autoregressive Conditional Heteroskedasticity Model) type models. Finally, a debate of the results is also provided.

Suggested Citation

  • Bentes, Sonia R & Menezes, Rui, 2012. "On the predictive power of implied volatility indexes: A comparative analysis with GARCH forecasted volatility," MPRA Paper 42193, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:42193
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/42193/1/MPRA_paper_42193.pdf
    File Function: original version
    Download Restriction: no

    References listed on IDEAS

    as
    1. Daly, Kevin, 2008. "Financial volatility: Issues and measuring techniques," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(11), pages 2377-2393.
    2. Martens, Martin & van Dijk, Dick, 2007. "Measuring volatility with the realized range," Journal of Econometrics, Elsevier, vol. 138(1), pages 181-207, May.
    3. Chen, En-Te (John) & Clements, Adam, 2007. "S&P 500 implied volatility and monetary policy announcements," Finance Research Letters, Elsevier, vol. 4(4), pages 227-232, December.
    4. Agnolucci, Paolo, 2009. "Volatility in crude oil futures: A comparison of the predictive ability of GARCH and implied volatility models," Energy Economics, Elsevier, vol. 31(2), pages 316-321, March.
    5. Latane, Henry A & Rendleman, Richard J, Jr, 1976. "Standard Deviations of Stock Price Ratios Implied in Option Prices," Journal of Finance, American Finance Association, vol. 31(2), pages 369-381, May.
    6. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    7. Chiras, Donald P. & Manaster, Steven, 1978. "The information content of option prices and a test of market efficiency," Journal of Financial Economics, Elsevier, vol. 6(2-3), pages 213-234.
    8. Nam, Seung Oh & Oh, SeungYoung & Kim, Hyun Kyung & Kim, Byung Chun, 2006. "An empirical analysis of the price discovery and the pricing bias in the KOSPI 200 stock index derivatives markets," International Review of Financial Analysis, Elsevier, vol. 15(4-5), pages 398-414.
    9. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
    10. Canina, Linda & Figlewski, Stephen, 1993. "The Informational Content of Implied Volatility," Review of Financial Studies, Society for Financial Studies, vol. 6(3), pages 659-681.
    11. Becker, Ralf & Clements, Adam E. & White, Scott I., 2006. "On the informational efficiency of S&P500 implied volatility," The North American Journal of Economics and Finance, Elsevier, vol. 17(2), pages 139-153, August.
    12. 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.
    13. 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.
    14. 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)

    More about this item

    Keywords

    implied volatility; volatility forecasts; GARCH models; volatility indices;

    JEL classification:

    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics

    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:42193. 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) or (Rebekah McClure). 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.