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New Evidence on the Information Content of Implied Volatility of S&P 500: Model-Free versus Model-Based

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Listed:
  • Weiwei ZHANG

    (Changchun University of Science and Technology, Changchun, China.)

  • Tiezhu SUN

    (Corresponding author. Changchun Guanghua University, Changchun, China.)

  • Yechi MA

    (School of Business, Northeast Normal University, Changchun, China.)

  • Zilong WANG

    (Department of Land Economy, University of Cambridge, Cambridge, UK.)

Abstract

This paper provides new evidence to compare the information content of model-free implied volatility (MFIV) and model-based volatility for forecasting future volatility of the S&P 500. We choose Black and Scholes (BS) implied volatility as our model-based volatility and VIX as our measure of MFIV. By using non-overlapping monthly samples from January 2004 to June 2019, we find that both BS implied volatility and MFIV are informationally efficient and subsume information contained in the historical realized volatility for forecasting future volatility. This is the first study show that BS implied volatility and MFIV contain the same information and there is no winner for forecasting future volatility. This implied that a forecast model could include both BS implied volatility and MFIV

Suggested Citation

  • Weiwei ZHANG & Tiezhu SUN & Yechi MA & Zilong WANG, 2021. "New Evidence on the Information Content of Implied Volatility of S&P 500: Model-Free versus Model-Based," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 109-121, December.
    • Handle: RePEc:rjr:romjef:v::y:2021:i:1:p:109-121
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    1. Mark Britten‐Jones & Anthony Neuberger, 2000. "Option Prices, Implied Price Processes, and Stochastic Volatility," Journal of Finance, American Finance Association, vol. 55(2), pages 839-866, April.
    2. Jeff Fleming & Barbara Ostdiek & Robert E. Whaley, 1995. "Predicting stock market volatility: A new measure," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 15(3), pages 265-302, May.
    3. Pierre Giot & Sébastien Laurent, 2007. "The information content of implied volatility in light of the jump/continuous decomposition of realized volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 27(4), pages 337-359, April.
    4. Dimos S. Kambouroudis & David G. McMillan & Katerina Tsakou, 2016. "Forecasting Stock Return Volatility: A Comparison of GARCH, Implied Volatility, and Realized Volatility Models," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(12), pages 1127-1163, December.
    5. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    6. Canina, Linda & Figlewski, Stephen, 1993. "The Informational Content of Implied Volatility," The Review of Financial Studies, Society for Financial Studies, vol. 6(3), pages 659-681.
    7. Bart Frijns & Christian Tallau & Alireza Tourani‐Rad, 2010. "The information content of implied volatility: Evidence from Australia," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 30(2), pages 134-155, February.
    8. Xin Cheng & Joseph K.W. Fung, 2012. "The Information Content of Model‐Free Implied Volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 32(8), pages 792-806, August.
    9. San‐Lin Chung & Wei‐Che Tsai & Yaw‐Huei Wang & Pei‐Shih Weng, 2011. "The information content of the S&P 500 index and VIX options on the dynamics of the S&P 500 index," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 31(12), pages 1170-1201, December.
    10. 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.
    11. Becker, Ralf & Clements, Adam E. & White, Scott I., 2007. "Does implied volatility provide any information beyond that captured in model-based volatility forecasts?," Journal of Banking & Finance, Elsevier, vol. 31(8), pages 2535-2549, August.
    12. S. Muzzioli, 2010. "Option-based forecasts of volatility: an empirical study in the DAX-index options market," The European Journal of Finance, Taylor & Francis Journals, vol. 16(6), pages 561-586.
    13. Taylor, Stephen J. & Yadav, Pradeep K. & Zhang, Yuanyuan, 2010. "The information content of implied volatilities and model-free volatility expectations: Evidence from options written on individual stocks," Journal of Banking & Finance, Elsevier, vol. 34(4), pages 871-881, April.
    14. George J. Jiang & Yisong S. Tian, 2005. "The Model-Free Implied Volatility and Its Information Content," The Review of Financial Studies, Society for Financial Studies, vol. 18(4), pages 1305-1342.
    15. 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.
    16. 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.
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    More about this item

    Keywords

    Implied volatility; VIX; Realized volatility; Information; Volatility forecasts; Volatility models;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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